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In 2021, corporations and law firms alike grappled with yet another year of disruption and unpredictability caused by economic volatility, a lingering global pandemic, increased regulation, and inequality within the workforce. To help our clients prepare for whatever 2022 may have in store, Lighthouse CEO Brian McManus welcomed economic forecaster and former CNBC chief economist and Squawk Box co-host Marci Rossell for a lively discussion centered around these current global macroeconomic trends, with a focus on their effect on the legal industry.Their conversation was wide-ranging and informative, touching on impacts, causes, and forecasts related to inflation, global workforce shortages, inequality in the workplace, technology adoption, and increased regulatory and data privacy restrictions. The key takeaways from this discussion are outlined below.Economic InflationAs of January 2022, the inflation rate was hovering around 7% in the United States (US), and around 5% in the European Union (EU). These are the highest inflation rates both countries have seen in decades. Rossell explained that one of the major contributing factors for this increase is the speed at which the overall economy recovered from the abrupt halt in economic activity in the spring of 2020 due to the COVID-19 pandemic. The sharp economic recovery drove a surge in demand for services and goods, at a time when supply around the world was at an all-time low due to pandemic-related shutdowns. This tension led to the current sustained inflation rates we’re seeing today, and those rates can be expected to remain high for the foreseeable future in markets where production is not expected to meet demand any time soon (such as the energy and oil industries).Within the legal industry, specifically, law firms and organizations have not only been impacted by the typical “cost of goods” inflation described above – they have also been impacted by inflation related to labor shortages and rising wages, as well as costs related to regulation and compliance. “The Great Resignation” and Its Impact on the Legal IndustryOver the last two years, droves of workers have switched employers, changed careers, or left the workforce all together, in what pundits and economists have deemed, “The Great Resignation.” Rossell explained that this global phenomenon may have roots in the financial crises of 2008 – 2009, when the economy contracted dramatically, leaving millennials struggling to enter a workforce plagued by an unemployment rate that had soared into the double digits. In the wake of this recession and for years afterward, the balance of power between employers and employees was weighted heavily in favor of employers, with overqualified workers applying to the same jobs, giving employers their pick of quality candidates. Now, this same generation of millennials have been confronted with a pandemic that has caused millions of people to suddenly sever their connections to jobs, employers, and/or geography. Many of these workers may not have felt very connected to where they worked or lived in the first place, but stayed because of their previous experience in a job market that was heavily influenced by the last recession. The pandemic suddenly forced this generation of workers into a situation that ultimately enabled them to make different career choices. And we are certainly seeing them making those choices. As Rossell noted, in addition to this trend among the millennial generation, the pandemic also escalated early retirements for an older generation, while an overall decrease in population growth has led to 400,000 fewer young people entering the labor force every year. These three factors are a perfect labor-shortage storm, with fewer experienced workers, fewer young people entering the labor market, and a generation of mid-career millennials reevaluating their careers and/or employers.McManus pointed out that labor shortage has also had a significant effect on the legal industry generally, and the eDiscovery industry specifically. eDiscovery is a niche industry, which makes it harder to find and retain experienced talent in general. But over the last twelve months, the tighter labor market has significantly exacerbated those issues. There is now a shortage of talent within eDiscovery and the cost of retaining valuable talent has sharply increased over the last nine months, with experienced employees being offered 20% to 40% more in compensation.This trend also affects the broader legal industry. Attrition of associates at law firms was at an unprecedented level in 2021 and the cost of retaining associates skyrocketed. For example, law firm associate compensation grew 11% in November of 2021, year over year, according to a state of the legal market report from the Thomson Reuters Institute. This trend can be expected to continue over the next few years due to the economic factors at play.To combat the worker shortage, McManus warned that employers should expect to not only offer higher compensation, but also include benefits like flexible work arrangements, in order to recruit and retain talented employees. Even prior to the pandemic, Rossell noted, studies showed that flexible work arrangement benefits were worth about 8% of a salary to younger employees. This trend is expected to sustain well into the future, as housing market trends indicate that 30-somethings are moving to larger homes away from large corporate offices and cities.Diversity, Equity, and Inclusion in the WorkforceThere has been a significant emphasis placed on diversity, equity, and inclusion (DE&I) over the past few years across many markets, including the legal industry. Rossell provided a historical perspective, explaining that thirty years ago the consensus from economists was that the labor market was rational and profit-maximizing and thus, discrimination in the labor force could not exist. The theory was that for-profit companies would always be incentivized to hire the best individual for the job, regardless of gender, race, ethnicity, sexual orientation, etc. But in 2004, a groundbreaking economic study on race in the labor market found that people with white-sounding names were 50% more likely to get a call back from an HR professional. This study was the beginning of a sea-change in economics, where organizations slowly realized the economic need for, and importance of, DE&I. In effect, organizations began to slowly understand that there was an economic cost to not hiring the best candidates, and that focusing on DE&I increases profitability, productivity, and growth.This sea-change is represented across the globe. European countries were initially on the forefront of this movement, as evidenced by the 2003 emphasis in Norway to have gender equity represented on corporate boards within the country. The US is now moving even further in that direction. Last year, Nasdaq proposed new board diversity rules and disclosure guidance, including that listed companies should have at least one board member who identifies as a woman, as well as one board member who self-identifies as an underrepresented minority or LGBTQ+.As McManus pointed out, this trend is also represented across the legal industry. There is a continued expectation for more diversity, equity, and inclusion within organizations, law firms, and legal technology supply vendors. Clients want to see diversity, equity, and inclusion represented in the teams they work with on a daily basis. Additionally, the next generation of talented employees is also demanding an equitable environment in which to work. Thus, legal and eDiscovery employers should expect that going forward, they will need to track, measure, and demonstrate an inclusive, equitable, and diverse environment in order to attract and retain the best workers.As Rossell pointed out: “(DE&I) matters to the next generation. As talent becomes scarcer and the balance of power shifts away from employers to employees, [the next generation of workers] is going to demand not only a flexible workforce but a diverse and inclusive environment to work in.”As DE&I programs advance, eDiscovery and legal teams will see how diverse hiring contributes to greater innovation and success.AI and Its Role in the Legal IndustryRossell also provided a historical view of technology innovation and its effect on worldwide economies. She noted that artificial Intelligence (AI) technology is the next step in a 200-year-old process that began with the industrial revolution – when advances in machine automation allowed simple machines to perform manufacturing related processes, enabling humans to migrate towards more service-related work. This has now evolved into machines that can now perform some of the work in the service sector, thanks to advancements in AI technology.McManus noted that within the legal industry, lawyers (who are trained to be risk-averse) have traditionally been much slower to adopt this emerging technology. However, the legal industry is also quickly becoming submerged in “big data,” and AI is one of the most effective tools to combat the labor shortages and increased costs that exacerbate the problems caused by massive data volumes. Nowhere is this more evident than in the document review process performed during eDiscovery.“The industry still follows a traditional approach [to document review] with large groups of lawyers reviewing massive volumes of text and that approach is just untenable,” McManus said.The impracticality of that traditional approach is not only due to the increased volume and complexity of data, but also due to labor shortages and increased labor costs. Advancements in AI give newer legal technology tools the capability to help automate and expedite the document review process. This should lead to AI adoption at a much faster pace than we’ve traditionally seen in the legal industry, McManus noted.The Global Regulatory Landscape, Anti-Trust Activity, and What to Look for in the Coming YearsRossell also provided an insightful overview of the dynamic and shifting regulatory landscape from an economist’s perspective. Increased governmental regulation is raising costs in almost every industry and is one of the driving forces behind higher inflation rates. In the United States, the increase in government regulation may be due to the fact that the government’s governing functions have been slowly shifting from the legislative branch to the executive branch. In turn, this shift means that every four years, companies may deal with a complete shift in the regulatory landscape depending on which political party wins the presidential office. These abrupt swings make compliance very costly and put pressure on smaller organizations. Often the only companies that can survive this type of volatility are those big enough to support a department solely dedicated to compliance. Thus, in some ways, increased government regulation is driving the consolidation of companies.At the same time, we are seeing a shift in antitrust policy from an economics perspective. Whereas previously, anti-competition policy was centered around whether consolidation would harm consumers, we’re now seeing a shift to assessing a broader range of harm. Prior to this shift, a merger would be blocked if it would cause higher prices for consumers (i.e., if the merger would cause consumers harm by giving them less choices and therefore raise consumer prices). Now, mergers are blocked for a much broader range of issues that are not just centered solely around consumers, but around society as a whole. For example, a merger might now be blocked if it would be harmful to the environment, to workers, would cause a decline in future competition, etc. This more aggressive governmental regulation worldwide is expected to continue in the coming years. In short, expect anti-competition scrutiny to continue to be broad and aggressive, regardless of changes in political parties and offices.The Future of the Global Data Privacy LandscapeFinally, McManus provided a helpful overview of recent changes to the data privacy landscape, and what to expect in the 2022. Another area where government regulation is expected to continue to increase globally is around data privacy rights and protections for consumers. The EU’s GDPR legislation in 2018 paved the way for data privacy rights, providing a template for governments on how to regulate and protect consumer data privacy. Within a few years, California followed suit, as did a plethora of other governments around the world. This trend is only expected to continue as we move into an increasingly digital world.In the US in 2021 alone, two more states passed comprehensive GDPR-like laws (Virigina and Colorado), while at least 25 other states introduced or had data privacy laws somewhere within the state legislative consideration process. And the US federal government also looks to be increasingly active in this area – with the U.S. House Energy and Commerce Committee voting to give the Federal Trade Commission $1 billion to set up a data privacy bureau. Even China passed a GDPR-like law in 2021, the Personal Information Protection Law, which included not only the risk of huge fines for non-compliance, but also the risk of companies being black-listed by the Chinese government.This focus on data privacy regulation will certainly increase costs for businesses in the coming years, as companies work to stay compliant with a patchwork of global and local data privacy laws and regulations.ediscovery-reviewccpa, gdpr, review, ai-big-data, blog, ediscovery-review,ccpa; gdpr; review; ai-big-data; bloglighthouse

Legal teams approaching a Hart-Scott-Rodino (HSR) Second Request may hold false assumptions about what is and isn’t possible with document review. Often these appear as necessary evils—compromises in efficiency and precision are inevitable given the unique demands of Second Requests. But, in fact, these compromises are only necessary in the context of legacy technology and tools. Using more current tools, legal teams can transcend many of these compromises and do more with document review than they thought possible.Document review during an HSR Second Request is notoriously arduous. Legal teams must review potentially millions of documents in a very short timeframe, as well as negotiate with regulators about custodians and other parameters that could change the scope of the data under review.Up until recently, legal teams’ ability to meet these demands was limited by technology. It wasn’t possible to be precise and thorough while also being extremely quick. As a result, attorneys adopted certain conventions and concessions around the timing of review steps and how much risk to accept.Technology has evolved since then. For example, tools powered by advanced artificial intelligence (AI) utilize deep learning models and big data algorithms that make review much faster, more precise, and more resilient than legacy tools. However, legacy thinking around how to prepare for Second Requests remains. Many attorneys and teams remain beholden to the constraints imposed on them by tools of the past. New review tools enable new approaches and benefits, eliminating these constraints. Here’s a look at three of the most common myths surrounding document review during Second Requests and how they’re proven false by modern review tools.Myth 1: Privilege review must come after responsive reviewThe classic approach to reviewing documents during a Second Request is to start by creating a responsive set and then review that set for privileged documents. This takes time— an extremely precious commodity during a Second Request—but these steps are unavoidable with legacy tools. The linear nature of legacy review models requires responsive review to happen first because supporting privilege review over an entire dataset simply is not a feasible task over potentially millions of records. Tools leveraging advanced AI, however, are well suited to support scalable privilege analysis with big data. Rather than save privilege review for later, legal teams can conduct privilege review simultaneously with responsive review. This puts documents in front of human reviewers sooner and shaves invaluable hours off the timeline as a whole.Myth 2: Producing privileged documents to regulators is inevitableInadvertently disclosing privilege documents to federal agencies is so common the Federal Rules of Civil Procedure give parties some latitude to do so without penalty. Even so, the risk remains of inadvertent disclosure during a Second Request that will invite additional questions and scrutiny from regulators and undermine the deal.Although advanced AI tools cannot eliminate the possibility of inadvertent disclosure, these automated solutions can vastly reduce it. In one recent Second Request, a tool using advanced AI was able to identify and withhold 200,000 privilege documents that a legacy tool had failed to catch. This spared the client from costly exposure and clawbacks.Myth 3: There’s no time to know the details of what you’re producingWith massive datasets and very little time to review, legal teams get used to producing documents without fully knowing what’s in them. This can cause surprise and pain down the line when regulators ask for clarification about information the team isn’t prepared to address.With advances in technology, teams can gain more clarity using tools that identify key documents. These tools conduct powerful searches of both text and document attributes, using complex and dynamic search strings managed by linguistic experts. Out of a million or more documents, key document identification can surface the one or two thousand that speak precisely to attorneys’ priorities, efficiently helping counsel prepare for testimony and other proceedings.What’s your Second Request strategy?Second Requests will always be intense. Advancements in eDiscovery technology prove the limits of the past don’t apply today. With technology moving beyond legacy tools, it is time for teams to move beyond legacy thinking as well.For more detail about how advancements in technology help teams meet the demands of Second Requests, download our eBook.antitrust; ediscovery-review; ai-and-analyticsreview, hsr-second-requests, blog, antitrust, ediscovery-review, ai-and-analytics,review; hsr-second-requests; blogkamika brown

Last year, California passed legislation that alters civil discovery procedures and significantly impacts discovery for all litigants in state court. This change in the state court rules of civil procedure essentially makes it mandatory for the producing party to identify the specific discovery request to which each and every document is responsive. Many fear this new rule will exponentially increase the cost and burden of discovery requests. The good news is there’s a simple solution: use technology to easily automate the process. In this blog, I’ll discuss a brief overview of the rule, the potential impact, and how technology can save the day and provide an automated and cost-effective solution.The RuleBeginning on January 1, 2020, California’s Code of Civil Procedure § 2031.280 was amended by legislation S.B. 370 to make it a requirement that documents planned for production identify “the specific request number to which the documents respond.” Prior to this rule (and as is the case in the majority of jurisdictions both in federal and state courts), documents could either be produced as they are maintained in the usual course of business, or organized to correlate with the categories in the discovery demand. By mandating this new way of organizing and labeling documents, S.B. 370 marks the establishment of a major new requirement for document productions and impacts all pending and active cases that are subject to California’s Civil Discovery Act. Of note, the new rule is vague on the procedural front and fails to identify how exactly litigants should fulfill the requirements, leaving open questions that courts will likely need to address in the future.Potential ImpactThe rule change is weighted towards the goal of saving the requesting party time and streamlining reviews so that large quantities of documents aren’t received without any indication of which discovery request they relate to. Litigants are also concerned, however, that a heavy burden in terms of time and cost is created by S.B. 370 for producing parties. Imagine a case involving a large-scale ESI production with thousands upon thousands of documents where the producing party must go through and manually identify every document and exactly which request it is responsive to. The time it would take to manually organize a large production at this level would almost certainly greatly increase the length of the review due to the challenge that is involved with manually determining how each document correlates to a specific discovery demand. Ultimately, the biggest potential impact of S.B. 370 is higher litigation costs as a result of a lengthened review if a manual process is left in place.The SolutionWhen contemplating the bigger burden this new rule might place on producing parties, there’s also a unique opportunity that presents itself. With the use of technology, large reviews can be managed with an automated solution that would decrease the time from review to production and reduce costs. At a high level, the solution would entail:Identify the Issues - Identifying a comprehensive list of issues involved in the review.Map the Issues - Once the issues are understood, they would be mapped to a numbered list of specific discovery requests.Review and Tag - Armed with that organizational structure, the reviewers would conduct their document review and tag the documents by issue as per usual. At the completion of the review, the solution would automatically link the documents to each category based on the original map we created at the commencement of the review.Report Back - A report could also be generated to be provided with the final production set. That list could be produced in a sortable spreadsheet or it could be automated to connect to separate tags within the review database so it could be searched as contemplated. With S.B. 370 now in effect, it’s important to set up an automated process that will address the changes and potentially create a better organized and more cost-effective review. ediscovery-reviewediscovery-process, blog, ediscovery-review,ediscovery-process; bloglighthouse

As the amount of data generated by companies exponentially increases each year, leveraging artificial intelligence (AI), analytics, and machine learning is becoming less of an option and more of a necessity for those in the eDiscovery industry. However, some organizations and law firms are still reluctant to utilize more advanced AI technology. There are different reasons for the reluctance to embrace AI, including fear of the learning curve, uncertainty around cost, and unknown return on investment. But where this is uncertainty, there is often great opportunity. Adopting AI provides an excellent opportunity for ambitious legal professionals to act as the catalysts for revitalizing their organization’s or law firm’s outdated eDiscovery model. Below, I’ve outlined a simple, five-step process that can help you build a business case for bringing on cutting-edge AI solutions to reduce cost, lower risk, and improve win rates for both organizations and law firms.Step 1: Find the Right Test CaseYou will want to choose the best possible test case that highlights all the advantages that newer, cutting-edge AI solutions can provide to your eDiscovery program.One of the benefits of newer solutions is that they can be utilized in a much wider variety of cases than older tools. However, when developing a business case to convince reluctant stakeholders – bigger is better. If possible, select a case with a large volume of data. This will enable you to show how effectively your preferred AI solution can cull large volumes of data quickly compared to your current tools and workflows.Also try to select a case with multiple review issues, like privilege, confidentiality, and protected health information(PHI)/personally identifiable information (PII) concerns. Newer tools hitting the market today have a much higher degree of efficiency and accuracy because they are able to run multiple algorithms and search within metadata. This means they are much better at quickly and correctly identifying types of information that would need be withheld or redacted than older AI models that only use a single algorithm to search text alone.Finally, if possible, choose a case that has some connection to, or overlap with, older cases in your (or your client’s) legal portfolio. For a law firm, this means selecting a case where you have access to older, previously reviewed data from the same client (preferably in the same realm of litigation). For a corporation, this just means choosing a case, if possible, that shares a common legal nexus, or overlapping data/custodians with past matters. This way, you can leverage the ability that new technology has to re-use and analyze past attorney work product on previously collected data.Step 2: Aggregate the Data Once you’ve selected the best test case, as well as any previous matters from which you want to analyze data, the AI solution vendor will collect the respective data and aggregate it into a big data environment. A quality vendor should be able to aggregate all data, prior coding, and other key information, including text and metadata into a single database, even if the previously reviewed data was hosted by different providers in different databases and reviewed by different counsel.Step 3: Analyze the Data Once all data is aggregated, it’s time for the fun to begin. Cutting-edge AI and machine learning will analyze all prior attorney decisions from previous data, along with metadata and text features found within all the data. Using this data analysis, it can then identify key trends and provide a holistic view of the data you are analyzing. This type of powerful technology is completely new to the eDiscovery field and something that will certainly catch the eye of your organization or your clients.Step 4: Showcase the Analytical ResultsOnce the data has been analyzed, it’s time to showcase the results to key decision makers, whether that is your clients, partners, or in-house eDiscovery stakeholders. Create a presentation that drills down to the most compelling results, and clearly illustrates how the tool will create efficiency, lower costs, and mitigate risk, such as:Large numbers of identical documents that had been previously collected, reviewed, and coded non-responsive multiple times across multiple mattersLarge percentages of identical documents picked up by your privilege screen (and thus, thrust into costly privilege re-review) that have actually never been coded privilege in any matterLarge numbers of identical documents that were previously tagged as containing privilege or PII information in past matters (thus eliminating the need for review for those issues in the current test case).Large percentages of documents that have been re-collected and re-reviewed across many mattersStep 5: Present the Cost ReductionYour closing argument should always focus on the bottom line: how much money will this tool be able to save your firm, client, or company? This should be as easy as taking the compelling analytical results above and calculating their monetary value:What is the monetary difference between conducting a privilege review in your test case using your traditional privilege screen vs. re-using privilege coding and redactions from previous matters?What is the monetary difference between conducting an extensive search for PII or PHI in your test case, vs. re-using the PII/PHI coding and redactions from previous matters?How much money would you save by cutting out a large percent of manual review in the test case due to culling non-responsive documents identified by the tool?How much money would you save by eliminating a large percentage of privilege “false positives” that the tool identified by analyzing previous attorney work product?How much money will you (or your client) save in the future if able to continue to re-use attorney work product, case after case?In the end, if you’ve selected the right AI solution, there will be no question that bringing on best-of-breed AI technology will result in a better, more streamlined, and more cost-effective eDiscovery program.ai-and-analyticsanalytics, ai-big-data, data-re-use, phi, pii, blog, ai-and-analytics,analytics; ai-big-data; data-re-use; phi; pii; bloglighthouse

This year’s International Women’s Day theme revolves around “breaking the bias” and imagining a more gender-equal world. This topic seems particularly relevant for the legal and technology fields, which both have long histories of being male-dominated industries. In 1980, just 8% of attorneys were women, with that number growing to 37% percent by 2021. While the number of women in the technology field has actually declined over the last 40 years, from 37% in 1985, to 33% in 2022.But cold statistics, while helpful, don’t tell the full story. Numbers can be helpful to get a 10,000-foot view of how far we’ve come and how far we still need to go—but they can’t tell us how to get to that gender-equal world or what it’s like to live those statistics. For that, we need to listen to women in the legal and technology space.We need to understand the perseverance of the women who broke through the glass ceiling when they were one of a few in the profession. Like when Supreme Court Justice Ruth Bader Ginsburg explained how they had to install a women’s bathroom in the justices’ robing room after her appointment to the Supreme Court in 1993. We need to hear the stories of the women who broke barriers while dealing with the intersectionality of gender and racial bias. Like Loretta Lynch, the first African-American woman and second woman to be confirmed as United States Attorney General in 2015, recounting the story of a client who directed all of his questions to Lynch’s co-worker – a young male associate – who had nothing to do with what Lynch was presenting.And we need to listen to the women leading our industry today and paving the way for the next generation. In that vein, Lighthouse is honored to feature seven women who are innovators, champions of equity, and models of leadership in the legal technology field:Vanessa Quaciari, eDiscovery Counsel, Baker Botts L.L.P.Kim Foster, Discovery Services Manager, Lane PowellKelly Clay, Assistant General Counsel and Global eDiscovery Counsel, GSKJani Grantz, eDiscovery Manager, DaVitaMarilyn Caldwell, eDiscovery Director, SiemensMoira Errick, Litigation Support Manager, StripeMargaret Dolson, Global Head eDiscovery Services and Archiving Technology, Deutsche Bank USAWe had the honor of interviewing these women about their experiences in the legal technology field and asking them their thoughts on breaking down biases within the industry. Their perspectives and advice can serve as a helpful guide for all people who strive for equality.Recognize the achievements and contributions of women Recognizing the achievements of women is a simple but powerful tool in the fight to break down bias. When women’s achievements, contributions, and ideas are recognized within a firm or organization, it helps dismantle harmful stereotypes that women are not as present in the workplace, or that they don’t achieve as much as men.Talking other women up is so important. When you have a seat at the table and an opportunity to promote another talented woman – you should always do so. —Margaret DolsonFrom a cultural perspective, you have to be intentional and lead by example. Elevate female voices by echoing their comments and ideas while ensuring they receive full credit for their contributions. Seek out their counsel in front of others, and do it often, so that it becomes the norm within your culture. —Kim FosterHowever, for a variety of reasons, women may not feel comfortable recognizing their own achievements. They may also be more reticent to accept recognition or downplay their contributions. Many of the women we spoke to mentioned that accepting recognition was just as important as giving it, because recognition of one woman serves to amplify the voices of others.Women are far too often dismissive of their own achievements. We don't want to be seen as someone who brags or calls attention to ourselves. Frequently, we fall into the societal trappings of even going so far as to be dismissive of our own accomplishments – if we even make them publicly known. I strive to normalize being proud of ourselves, to share what we have achieved, and know that even if it may seem small to our own eyes, it's an accomplishment. I encourage a safe and supportive environment where everyone can feel free to share in their own way, through their own voice, or through the help of another. We all deserve recognition for what we do. —Moira ErrickI remind women that your achievements may seem like just doing your job, but they are so much more for each of us, and it is important to accept and recognize the appreciation. —Kelly ClayI’ve joined organizations to get my name, knowledge, and experience out there to show what women are capable of and be encouraging to women and other genders. —Jani GrantzTo help facilitate and encourage this recognition, it’s important for firms, organizations, departments, and teams to have a dedicated method for acknowledging achievements, wins, and contributions for all employees. This can be as simple as an email chain, or as formal as a dedicated system.My company as a whole strives for equality in all areas, be it gender, race, or any other identifying factor, and that allows my team the ability to recognize accomplishments from everyone including women. In my department, we do Friday emails where people get shout-outs for their contributions and wins, all inclusive of genders, as everyone’s achievements are important to the growth of the village. —Jani GrantzWe are proud to have extremely talented women throughout our firm and are constantly making sure we help raise their visibility. —Vanessa QuaciariWe celebrate achievements both formally and informally, including day-to-day support and recognition in broader team meetings, postings, and events. —Marilyn CaldwellWork to increase representation Both the legal and technology fields have been historically male-dominated. While the statistics are improving incrementally, there is still a way to go before there is equity in the legal technology industry.Many times in my career, I have been the only woman in the room, in the meeting, in the planning session. —Marilyn CaldwellGenerally speaking, both the legal and technology fields have up to now been male-dominated. Even in the eDiscovery niche, the technological knowhow is typically something that is provided by men. This likely is the result of the relatively low number of women historically graduating with science, technology, engineering, and mathematics (STEM)-related degrees. —Vanessa QuaciariHistorically, there has been a perception that women are not as technically inclined or analytical as men. This is simply not true, evidenced by the many exceptional women in eDiscovery at all levels. The legal and technology fields both suffer from stereotypes of having fewer women in them than many other fields. While more women have been entering law school and the legal field generally, there are fewer women at the higher levels of ownership (partners) and leadership. Women want equitable opportunities for growth and development, and they want to be considered for leadership roles. —Kim FosterOver the years I’ve seen men get bigger matters, better pay, and faster promotions because “historically men know more about technology” and they support their own first. —Jani GrantzThus, the importance of women representation in the industry cannot be understated. A more diverse team is stronger and more innovative. Representation also breaks down barriers and moves organizations toward gender equality.When there are more of us in the room, more women who have a seat at the table and have the ability to influence decision-making, it puts us in a better position to recognize the potential of other women and help move them forward. —Margaret DolsonMore women in leadership positions bring a more well-rounded, balanced, and holistic perspective to business. —Marilyn CaldwellThere are a variety of ways to increase representation of women, both on a small scale and across the entire industry. On a micro level, team members can ensure that there is diversity across projects, matters, and teams. Co-workers can prioritize diversity of thought when setting important meetings. Outside of work, people can strive to improve representation by getting involved in technology and legal education programs or join industry groups dedicated to diversity, equity, and inclusion in the field. On a macro level, organizations should develop systems to ensure their hiring, pay, and career development practices are driving diversity. Companies and firms can also support organizations that are dedicated to increasing diversity in technology and legal education.We get to increased representation in the industry by listening, by intentional discourse, and, most importantly, by supporting and identifying women with talent to fill these roles.—Marilyn CaldwellBreaking gender biases starts at home. I have two daughters and a son, and I try to instill in them all an interest in science and technology, rather than perpetuate the misguided notion that those fields are only appropriate for boys. —Vanessa QuaciariTake stock of your current compensation program (i.e., how are people paid, do we have consistent methodologies to establish pay ranges for a specific role, provide pay increases, etc.). Develop hiring and recruiting protocols that evaluate individuals based on observable skills, measurable outcomes, etc. In hiring, this may entail ensuring that recruiters use similar questions for each candidate, improve validity and reliability within the candidate selection process, and give weight to candidate attributes that actually count and ensure that scorers are consistent. —Kim FosterI personally have worked to change that gender stereotype by increasing my eDiscovery tech knowledge, learning the front and back end of relevant software, getting my RCA, and staying current with legal tech updates. —Jani GrantzBefore implementing these systems at the organizational level, however, decision-makers may need to be trained to understand their own implicit biases to ensure they are not unintentionally hampering diversity efforts. Educate your decision-makers about bias and implicit bias. Decision-makers could include, but are not limited to, your organization’s recruiting team, hiring managers, supervisors, those in leadership roles who hire individuals, including positions responsible for ongoing professional development. —Kim FosterOne of the things I’ve championed within our organization is unconscious bias training and exposure – because I think the awareness of that is what can really lead to change. Discussing unconscious bias and its effects is not about assigning blame. It’s about talking through the things that may cause us to be inherently biased against others, and even ourselves, within the workplace. And that discussion can lead us to shift those perceptions so that everyone feels comfortable expressing their thoughts and opinions. —Margaret DolsonBoldly be yourself… and then don’t be afraid to use your voice loudlyMany high-achieving women often speak about facing “imposter syndrome” – the feeling of doubting your own ability in a role while feeling like a fraud masquerading as a leader. This experience may be exacerbated for women in a male-dominated industry because other leaders and experts in the industry are predominantly men, and therefore, don’t look or sound like they do.One way to overcome this feeling is to recognize the implicit bias you may have around what an “expert” or “leader” looks or sounds like – and then working to stop trying to fit into that mold. In other words, strive to be your authentic self.Imposter syndrome is a very real issue because we may never fit into the template of what a “leader” has traditionally looked and sounded like within the legal and technology industries. So, we end up trying to fit into a mold of someone who is not remotely like us. But when we are able to be our authentic selves, and we know our subject matter – we can show up as competent, charismatic, and confident even when we don’t fit into a blueprint. However, it can take a lot of courage to do that. —Margaret DolsonOnce you are not afraid to use your own voice, you can then start using it loudly – not only to demonstrate your own expertise and knowledge, but also as a voice for others.Present yourself as you are, focusing on your skills and abilities rather than your appearance. Do not be afraid to put yourself “out there” for technical positions or projects, and never let anyone tell you that you are not capable. —Kim FosterContinue to stand up for gender equality and don’t back down whether you’re a woman who is being treated unfairly or someone who is witnessing acts of inequality toward women and other genders. Don’t be afraid to voice your opinion and bring notice to the bias. Even if it’s unintentional, it’s important that people see the affects bias has so that behaviors can be changed. —Jani GrantzDon’t let inertia get you. Speak up, advocate for yourself the way you would for others. Take up more space than you need and keep moving forward. —Kelly ClayThere are very brilliant women who are leading the charge both on the legal and the technological side as well as the judicial side. Day in and day out they are demonstrating through case law, articles, and innovative technology expansion that the traits we prize in the workforce are equal opportunity characteristics that any human can demonstrate passionately. —Moira ErrickLean in. Gather perspective. Be clear. Be diplomatic AND assertive. Be an example. Take a seat at the table. Be brave. Be candid. Listen to understand. —Marilyn CaldwellFind your tribeIt’s important to find your “tribe” – a group of people who support each other and can provide knowledgeable advice and an ear to listen when needed. When women have a support system and feel accepted as they are, they feel comfortable using their voice to advocate for themselves and for others. In this way, women can empower each other to break through barriers and bias.I strongly urge all women to find their tribe. Find a mentor, be a mentor. Be active in both your professional and personal communities in whatever way you can. We don't have to network through these organized functions to be supported. We can support one another on the sidelines of the soccer field, at 3 a.m. on a group text as we cram in one more rewrite of that summary, or at 8 a.m. as we take a moment to ourselves. Find your tribe who will give you the support and respect we all deserve. —Moira ErrickWithin the workplace, I recommend women align themselves with similarly-minded professionals, not only women in leadership positions, but people whose careers and knowledge are worth emulating and understanding. I think this helps break gender biases while creating goodwill with people with similar career paths. —Vanessa QuaciariRecognize the historic challenges women are facing today – and work to overcome thoseCovid-19 has had a dramatic effect on the workforce. But it has had a disproportionate effect on women. For instance, a 2021 policy brief from the International Labour Organization found that globally, women’s employment dropped by 4.2% between 2019 and 2020, compared with 3% for men. And a January 2021 report from the National Women’s Law Center showed that when the economy lost 140,000 net jobs in December of 2020, all of those losses fell on women (with women losing 156,000 jobs and men gaining 16,000). This disproportionate effect is because women are often the primary caregivers in family structures.Covid has impacted all of us profoundly. For caregivers in a family its impact is amplified. I don’t want to assume that all caregivers are women, but many are the primary caregivers and also have full time jobs. —Kelly ClayAs a mother, I am aware of how the pandemic has impacted not only women lawyers with children, but parents in general, who now have their usual load of professional responsibilities plus the added duties related to having their children at home all of the time. —Vanessa QuaciariI have seen many working women, especially those who also act as caregivers, facing a lot of added stress due to biased thinking. I have seen many women who have had to make life altering choices...family or career. Near and dear friends have had to step away from their roles because they are not afforded the trust by their employers to get their jobs done outside of the “correct” hours of the day. Covid has exacerbated that, but by the same token it has brought this issue to the forefront. It's not a problem that is unique to any one company, it is endemic in our nation. —Moira ErrickIndeed, while these hardships were felt most acutely during pandemic-related lockdowns, the pandemic simply highlighted and exacerbated inequities that already existed for women. Moving forward, this can be addressed by looking more holistically at the root cause and working to remedy from the ground up. In terms of how to curb the disproportionate impact of the pandemic as we move forward – we need to shift our focus to include men in this analysis. Rather than solely asking women what they need, we also need to ask men, “What do you need in order to be equal participants in running a household?” Because running a household is very similar to running a business and when we focus only on women, we are saying that it’s solely on a woman to keep that business running. The disproportionate burden on women can’t just be addressed by trying to accommodate women, we need to also bring men into the equation. —Margaret DolsonCorporations that support work life balance, in whatever terms the employee sets, are still unicorns. We have to recognize, as a nation, that the mindset that work can only be done in one location during set hours is simply not true in today's business world and given the disparate impact such restrictions have on women, it should not be tolerated anymore. We cannot close the door to half of the workforce because they are left with no other options due to inability to access childcare, lack of school, partners who also are beholden to unforgiving work schedules, and the many other hurdles that are out there. We need to recognize that work is work, whether it is done between 9 a.m. and 5 p.m. or 7 p.m. to 2 a.m. or any combination thereof, so long as it meets the overarching needs of the business. —Moira ErrickConclusionThe stories and advice of these women leaders can serve as a guide, helping to lead us to become a more gender-equal industry and world. Lighthouse is proud to amplify their voices.diversity-equity-and-inclusionblog, diversity-equity-and-inclusion,bloglighthouse

self-service, spectra models are becoming increasingly more popular within the eDiscovery space. The ability to easily manage matters using in house teams, not only saves time and money, but it also allows companies to scale and maintain control in the ever-growing data landscape we live in today, without having to make the investment in infrastructure or additional headcount. It is no wonder so many firms and corporations are making the shift to a technology on-demand model and upgrading their internal processes.However, whether you are hoping to move away from a legacy platform or looking to upgrade your current self-service, spectra tool-kit, the process can seem intimidating and may take some convincing for those not completely on board. Below I outline some key steps to help you build a business case to propose to your teams and get the ball rolling when it comes to onboarding or upgrading your self-service, spectra practice.1. Assess the Interests of the Decision Makers – This is the first key step to getting started and will help you build your business case moving forward. To get started, list the current challenges your key decision makers are facing and whether they can be addressed with an upgraded self-service, spectra model. If folks are unsure, review the key benefits of modern self-service, spectra solutions and explore if any of them resonate.2. Outline the Goal – Once you understand the interests of your decision makers, the next step is to identify their key needs and requirements. For example, what matters most to your team? Is it accessibility, speed, data analytics, scalability, cost recovery, all-in-one tool, ease of use, low maintenance, controlled access, limited professional service hours, etc.? Define their top requirements and overall goals, and keep those top of mind while executing the next few steps.3. Do the Research – Next, dig into those challenges and key requirements and how an upgraded self-service, spectra model may meet those needs. Why will this model be of value to your team, and more specifically what are the top benefits and how do those overlap with the decision maker’s needs? Are there any client success stories that are relatable to your team’s situation? Stats?4. Develop the Pitch – Once you have conducted your research and have a solid list of key findings and benefits, outline them in a digestible manner. Think competitive matrices, tables, and PowerPoint. Feel free to leverage this Excel or PDF self-service, spectra selection matrix template. Lay out the key reasons why an upgrade makes sense and how it will meet the needs of your team.5. Present the Findings – Prior to presenting, get a meeting invite on the books with details and expectations (i.e. looking for decision maker’s feedback and preferences). It is also a good idea to preview the findings with your leader or a trusted colleague who can weigh in and provide ideas to enhance your presentation. Present your findings, any key client success stories you uncovered, as well as the benefits that matter most to your team.6. Continue the Communication – After the presentation, be sure to follow up and address any concerns or questions that came up in the meeting. If needed, set another meeting to hone in on some of those questions. Ask for feedback and continue the conversation.Building a business case to upgrade your self-service, spectra practices can require upfront research and tough conversations, but this simple six-step guide should ease that process. To discuss these steps further or for assistance developing your business case, feel free to reach out to me at bthompson@lighthouseglobal.com.ediscovery-reviewcloud, self-service, spectra, blog, ediscovery-review,cloud; self-service, spectra; blogbrooks thompson

It’s no secret that big data can mean big challenges in the eDiscovery world. Data volumes and sources are exploding year after year, in part due to a global shift to digital forms of communication in working environments (think emails, chat messages, and cloud-based collaboration tools vs. phone calls, in-person meetings, and paper memorandums, etc.) as well as the rise of the Cloud (which provides cheaper, more flexible, and virtually limitless data storage capabilities).This means that with every new litigation or investigation requiring discovery, counsel must collect massive amounts of potentially relevant digital evidence, host it, process it, identify the relevant information within it (as well as pinpoint any sensitive or protected information within that relevant data) and then produce that relevant data to the opposing side. Traditionally, this process then starts all over again with the next litigation – often beginning back at square one in a vacuum by collecting the exact same data for the new matter, without any of the insights or attorney work product gained from the previous matter.This endless cycle is not sustainable as data volumes continue to grow exponentially. Fortunately, just as advances in technology have led to increasing data volumes, advances in artificial intelligence (AI) technology can help tackle big data challenges. Newer analytics technology can now use multiple algorithms to analyze millions of data points across an organization’s entire legal portfolio (including metadata, text, past attorney work product, etc.) and provide counsel with insights that can improve efficiency and curb the endless cycle of re-inventing the wheel on each new matter. In this post, I’ll outline the four main challenges big data can pose in an eDiscovery environment (also called “The Four Vs”) and explain how cutting-edge big data analytics tools can help tackle them.The “Four Vs” of Big Data Challenges in eDiscovery 1. The volume, or scale of dataAs noted above, a primary challenge in matters involving discovery is the sheer amount of data generated by employees and organizations as a whole. For reference, most companies in the U.S. currently have at least 100 terabytes of data stored, and it is estimated that by 2025, worldwide data will grow 61 percent to 175 zettabytes.As organizations and individuals create more data, data volumes for even routine or small eDiscovery matters are exploding in correlation. Unfortunately, court discovery deadlines and opposing counsel production expectations rarely adjust to accommodate this ever-growing surge in data. This can put organizations and outside counsel in an impossible position if they don’t have a defensible and efficient method to cull irrelevant data and/or accurately identify important categories of data within large, complex data sets. Being forced to manually review vast amounts of information within an unrealistic time period can quickly become a pressure cooker for critical mistakes – where review teams miss important information within a dataset and thereby either produce damaging or sensitive information to the opposing side (e.g., attorney-client privilege, protected health information, trade secrets, non-relevant information, etc.) or in the inverse, fail to find and produce requested relevant information.To overcome this challenge, counsel (both in-house and outside counsel) need better ways to retain and analyze data – which is exactly where newer AI-enabled analytics technology (which can better manage large volumes of data) can help. The AI-based analytics technology being built right now is developed for scale, meaning new technology can handle large caseloads, easily add data, and create feedback loops that run in real time. Each document that is reviewed feeds into the algorithm to make the analysis even more precise moving forward. This differs from older analytics platforms, which were not engineered to meet the challenges of data volumes today – resulting in review delays or worse, inaccurate output that leads to critical mistakes.2. The variety, or different forms of dataIn addition to the volume of data increasing today, the diversity of data sources is also increasing. This also presents significant challenges as technologists and attorneys continually work to learn how to process, search, and produce newer and increasingly complicated cloud-based data sources. The good news is that advanced analytics platforms can also help manage new data types in an efficient and cost-effective manner. Some newer AI-based analytics platforms can provide a holistic view of an organization’s entire legal data portfolio and identify broad trends and insights – inclusive of every variety of data present within it. These insights can help reduce cost and risk and sometimes enable organizations to upgrade their entire eDiscovery program. A holistic view of organizational data can also be helpful for outside counsel because it also enables better and more strategic legal decisions for individual matters and investigations.3. The velocity, or the speed of dataWithin eDiscovery, the velocity of data not only refers to the speed at which new data is generated, but also the speed at which data can be processed and analyzed. With smaller data volumes, it was manageable to put all collected data into a database and analyze it later. However, as data volumes increase, this method is expensive, time consuming, and may lead to errors and data gaps. Once again, a big data analytics product can help overcome this challenge because it is capable of rapidly processing and analyzing iterative volumes of collected data on an ongoing basis. By processing data into a big data analytics platform at the outset of a matter, counsel can quickly gain insights into that data, identifying relevant information and potential data gaps much earlier in the processes. In turn, this can mean lower data hosting costs as objectively non-responsive data can be jettisoned prior to data hosting. The ability of big data analytics platforms to support the velocity of data change also enables counsel and reviewers to be more agile and evolve alongside the constantly changing landscape of the discovery itself (e.g., changes in scope, custodians, responsive criteria, court deadlines).4. The veracity, or uncertainty of dataWithin the eDiscovery realm, the veracity of data refers to the quality of the data (i.e., whether the data that a party collects, processes, and produces is accurate and defensible and will satisfy a discovery request or subpoena). The veracity of the data produced to the opposing side in a litigation or investigation is therefore of the utmost importance, which is why data quality control steps are key at every discovery stage. At the preservation and collection stages, counsel must verify which custodians and data sources may have relevant information. Once that data is collected and processed, the data must then be checked again for accuracy to ensure that the collection and processing were performed correctly and there is no missing data. Then, as data is culled, reviewed, and prepared for production, multiple quality control steps must take place to ensure that the data slated to be produced is relevant to the discovery request and categorized correctly with all sensitive information appropriately identified and handled. As data volumes grow, ensuring the veracity of data only becomes more daunting.Thankfully, big data analytics technology can also help safeguard the veracity of data. Cutting-edge AI technology can provide a big-picture view of an organization’s entire legal portfolio, enabling counsel to see which custodians and data sources contain data that is consistently produced as relevant (or, in the alternative, has never been produced as relevant) across all matters. It can also help identify missing data by providing counsel with a holistic view of what was collected in past matters from data sources. AI-based analytics tools can also help ensure data veracity on the review side within a single matter by identifying the inevitable inconsistencies that happen when humans review and categorize documents within large volumes of data (i.e., one reviewer may categorize a document differently than another reviewer who reviewed an identical or very similar document, leading to inconsistent work product). Newer analytics technology can more efficiently and accurately identify those inconsistencies during the review process so that they can be remedied early on before they cause problems. Big Data Analytics-Based MethodologiesAs shown above, AI-based big data analytics platforms can help counsel manage growing data volumes in eDiscovery.For a more in-depth look at how a cutting-edge analytics platform and big data methodology can be applied to every step of the eDiscovery process in a real-world environment, please see Lighthouse’s white paper titled “The Challenge with Big Data.” And, if you are interested in this topic or would like to talk about big data and analytics, feel free to reach out to me at KSobylak@lighthouseglobal.com.ai-and-analytics; ediscovery-reviewcloud, analytics, ai-big-data, ediscovery-process, prism, blog, ai-and-analytics, ediscovery-reviewcloud; analytics; ai-big-data; ediscovery-process; prism; blogkarl sobylak

The current state of eDiscovery is complex, inefficient, and cost prohibitive as data types and volumes continue to explode without bounds. Organizations of all sizes are bogged down in enormous amounts of unresponsive and duplicative electronically stored information (ESI) that still make it to the review stage, persistently the most expensive phase of eDiscovery.Data is at the center of this conundrum and it presents itself in a number of forms including:Scale of Data - In the era of big data, the volume, or amount of data generated, is a significant issue for large-scale eDiscovery cases. By 2025, IDC predicts that 49 percent of the world’s stored data will reside in public cloud environments and worldwide data will grow 61 percent to 175 zettabytes.Different Forms of Data - While the volume of ESI is dramatically expanding, the diversity and variety are also greatly increasing, and a big piece of the challenge involved with managing big data is the varying kinds of data the world is now generating. Gone are the days in eDiscovery where the biggest challenge was processing and reviewing structured, computer-based data like email, spreadsheets, and documents.Analysis of Data - Contending with large amounts of data creates another significant issue around the velocity or speed of the data that’s generated, as well as the rate at which that data is processed for collection and analysis. The old approach is to put everything into a database and try to analyze it later. But, in the era of big data, the old ways are expensive and time-consuming, and the much smarter method is to analyze in real time as the data is generated.Uncertainty of Data - Of course, with data, whether it’s big or small, it must be accurate. If you’re regularly collecting, processing, and generally amassing large amounts of data, none of it will matter if your data is unreliable or untrustworthy. The quality of data to be analyzed must first be accurate and untainted.When you combine all of these aspects of data, it is clear that eDiscovery is actually a big data and analytics challenge!While big data and analytics has been historically considered too complex and elaborate, the good news is that massive progress has been made in these fields over the past decade. Access to the right people, process, and technology in the form of packaged platforms is more accessible than ever.Effective utilization of a robust and intelligent big data and analytics platforms enable organizations to revamp their inefficient and non-repeatable eDiscovery workflows by intelligently learning from past cases. A powerful big data and analytics tool utilizes artificial intelligence (AI) and machine learning to create customized data solutions by harvesting data from all of a client’s cases and ultimately creating a master knowledge base in one big data and analytics environment.In particular, the most effective big data and analytical technology solution should provide:Comprehensive Analysis – The ability to integrate disparate data sources into a single holistic view. This view gives you actionable insights, leading to better decision making and more favorable case outcomes.Insightful Access – Overall and detailed visibility into your data landscape in a manner that empowers your legal team to make data-driven decisions.Intelligent Learnings – The ability to learn as you go through a powerful analytics and machine learning platform that enables you to make sense of vast amounts of data on demand.One of the biggest mistakes organizations make in eDiscovery is forgoing big data and analytics to drive greater efficiency and cost savings. Most organizations hold enormous amounts of untapped knowledge currently locked away in archived or inactive matters. With big data and analytics platforms more accessible than ever, the opportunity to learn from the past to optimize the future is paramount.If you are interested in this topic or just love to talk about big data and analytics, feel free to reach out to me at KSobylak@lighthouseglobal.com.ai-and-analytics; ediscovery-reviewai-big-data, blog, ai-and-analytics, ediscovery-reviewai-big-data; blogkarl sobylak

It’s an exciting time in the world of legal tech as SaaS eDiscovery solutions, and cloud computing in general, represent an enormous amount of potential with nearly unlimited capacity of storage, power, and scalability, whether you’re handling small or very large matters. Once seen as something only big firms need to deal with for large cases, we’ve seen electronic communication in the workplace (like email and chat) become the norm and consequently eDiscovery become a typical domain for law firms of all shapes and sizes. It makes perfect sense that the proliferation of an easy-to-use, cost-effective solution is the future for an industry right on the cusp of its next iteration.So you’re ready to embrace this next era of eDiscovery and you’ve decided to adopt a SaaS, self-service, spectra tool within your firm? In my previous blog, I outlined three top reasons why SaaS makes the most sense for law firms in the age of cloud storage, especially as new and improved self-service, spectra tools incorporate the latest technology, are easy to use, and have significantly improved the efficiency of the typically arduous and expensive on-prem eDiscovery process.But transitioning even some of your firm’s in-house eDiscovery process to a SaaS solution requires careful thought around the complexities involved with security, solution support, and business continuity. To make the transition to SaaS as smooth as possible, it’s important to tailor your solution to your specific environment and create an implementation plan that will set you up for success. Here are a few suggestions for best practices to consider when you’re ready to embrace the self-service, spectra, SaaS eDiscovery revolution and leave your cumbersome on-prem environment behind.Eliminate your on-prem applications and infrastructure. Many firms have a patchwork of on-prem tools that they use for different phases of their eDiscovery workflow. A great starting point to eradicating the expense, headache, and risk that comes with maintaining your own infrastructure is to get rid of those old on-prem applications altogether and start fresh with a SaaS tool that will handle your entire workflow. That means choosing one comprehensive tool that allows you to create, upload, and process matters while also enabling you to manage your users across matters and locations from a single place. You’ll not only eliminate administrative headaches, you’ll no longer have to worry about managing data and will be free to concentrate on analyzing data while your SaaS solution provider takes on the security and infrastructure management for you.Leverage best-of-breed tools. A common problem for consumers of on-prem eDiscovery software has been needing to pull together multiple technologies to process, review, perform analytics, and produce data. While you’ve been working with that complicated patchwork of tools you’ve licensed and tried to maintain within your own IT environment, new versions of best-of-breed tools have evolved for everything from processing to analytics to review and production. Now that you’ve chosen one SaaS tool that can handle your full eDiscovery workflow, your new tool should provide you with access to the most updated and advanced tools across the EDRM without any maintenance or upgrades ever needing to be managed on your end.Ensure your solution is supported. Once you’re on board with a streamlined eDiscovery workflow with no infrastructure risks or administrative headaches and access to the most modern and best available eDiscovery tools, what happens when a matter becomes too large or unwieldy or you simply need access to a more traditional full-service support model? In this case, make sure you’re set up with a SaaS tool that is supported by a solution provider who can easily transition you from that self-service, spectra, on-demand eDiscovery model to one where they can take over when you need them to. In addition, speed to implementation is something to consider. While on-prem systems can take months to actually install and implement, a self-service, spectra, SaaS tool can literally be up and ready to use within days.Now that you’ve made the smart decision to modernize your eDiscovery program and implement a self-service, spectra, SaaS solution, it’s time to use these best practices to eliminate your expensive and risky infrastructure, streamline your workflow, adopt the most advanced best-in-breed tools, and benefit from a self-service, spectra tool that’s also backed with the peace of mind of full support from your solution provider. ediscovery-review; ai-and-analyticscloud, self-service, spectra, blog, ediscovery-review, ai-and-analyticscloud; self-service, spectra; bloglighthouse

Legal operations departments aim to support the delivery of legal services in an efficient manner. To that end, resource management and solving problems through technology are core responsibilities of the department. But, the tasks of a legal department vary from answering legal phone calls, filing patents, reviewing and approving contracts, and litigating, just to name a few. With such a varied workload, what to automate can be difficult to identify. To help, I have put together a brief overview of where to start.Step 1: IdentificationStart by identifying the tasks that are repetitive. One of the best ways I have found to do this is to set up a quick 15-minute discussion with 3-5 representatives from different functional areas of your legal team, and from different levels (e.g. individual contributor, manager, function head). In that meeting, ask them one or all of the following questions:What tasks do you wish your team no longer had to do?What tasks do you want to be replaced by robots in the future?What tasks are low value but your team still spends a lot of time on?You should not spend too much time here – the goal is to identify a pretty quick list that is top of mind for people. From these interviews, create a list for further vetting. Just in case you come up empty handed or aren’t able to get time with people within legal, here is a list of items that are commonly automated and we would expect to come up:Contract Automation Self service retrieval of boilerplate contracts (e.g., NDAs) Self service building common contracts (e.g., clause selection for vendor contracts, developer agreements)Request for review, negotiation, and signature of other contractsLegal Team Approvals Marketing document approvals Budget approval for any legal team spend Legal Assistance Requests (Intake) Legal research request Legal advice on an issue neededNeed for outside counselPatent Management Alerts for filing and renewal deadlines Automatically manage workflow for submissionsSelect one or two items from your list and then validate it with your boss and/or general counsel. You want to understand whether others agree on the impact automation will make and identify any potential concerns.Step 2: Build vs. BuyWhether to purchase third-party software or build your own internally is always a good question to start with. Building your own tool gives you exactly what you want with, oftentimes, very little need to change your process. But, it is more resource-intensive both for the build and the maintenance. Buying off the shelf software limits you in what’s commercially available but it takes all the load off your development resources.For some, build or buy may be an easy question as they may not have access to development resources. For others, they may not have any budget for an external tool and/or may be required to use internal teams. For most, however, they fall in the middle and have some access to resources and some budget (but usually not enough of either – that’s a whole other topic).If you fall into this latter category, you will have to analyze your options. Your organizational culture will dictate what depth of analysis is needed. Regardless of the level of detail, the process is the same. The easiest place to start is by surveying what is commercially available. Even if you decide to build, knowing what software is out there, what features are available, and the general costs is helpful. Next, it is helpful to get an approximate cost of the build and maintenance if done internally. This can be a rough order of magnitude based on estimates from other internal tools developed or can be a more detailed estimate developed with the engineering team. Once you have the costs, you will want to add some information about the pros and cons of each solution – e.g., time to build and implement, technology dependencies (if known), other considerations (e.g., we are moving to the cloud in 6 months and we don’t know impact). Once you have this analysis, you can put forth a recommendation to your boss and whomever else is required to decide on how to proceed.Step 3: DesignNow that you have a decision, you can move on to design. This is the most critical stage as this is where you are determining exactly what results your automation will produce. The first thing to do here is to map out your current internal process including who does what. You want to make sure you have a representative of each group take a look at the process diagram and validate it.Once you have the process in place, you’re ready to work with the development team. If you are buying a solution for automation, you should be working closely with the software provider’s onboarding team to overlay your current process with the capabilities of the software. You will want to note where the software does not support your process and where changes will need to be made. If you adjust your process, be sure to involve the same representatives that helped with the initial diagram to provide feedback on any proposed changes in the process.If you are building the solution, you will meet with your internal product resource. This person (or people) will want to understand the process diagram and may even want to watch people go through the process so they can understand user behavior. They will then likely convert your diagram into user stories that developers will develop against. Make sure to be as specific as possible in this process. This resource will be the one representing your voice with the developers so you want them to really understand the nuances of the process.Expect some iteration back and forth during this stage and although I have simplified it here, this will be a long stage and the most important.Step 4: ImplementationThe final stage of the process is implementation. Start with a pilot of the automation. Either select a small use case or a small group of users and validate that your automation functions as planned. During this pilot project, it is really helpful to have resources from your software providers or from the development team readily available to make changes and help answer questions. During this pilot, you should also keep track of how the automation is performing versus your expectations. For example, if you expected it to save time, create a way to track the time it saves and report on that metric.After a successful pilot and necessary refinement, you can move on to your full rollout. Create a plan that includes the deployment of the technology, training, feedback, and adjustment. Make sure to also identify the longer-term maintenance strategy that includes continuing to gather feedback and ways to improve the automation over time.There are lots of great publications that go into further detail about each of the steps above, but hopefully this points you in the right direction. Once deployed, automation can be a very powerful tool that augments your team without adding additional FTEs.To discuss this topic more, please feel free to reach out to me at DJones@lighthouseglobal.com.legal-operations; ai-and-analyticslegal-ops, blog, legal-operations, ai-and-analyticslegal-ops; bloglighthouse

When it comes to data privacy in healthcare fraud investigations and litigation, there is more than HIPAA to consider. Fraud investigations and litigation in the healthcare industry are growing. Whether these matters are handled internally or involve external parties to produce to, increased regulatory scrutiny — coupled with vast amounts of data generated by healthcare organizations — has created a pressing need for such organizations to become more adept at comprehensively inventorying, accessing, and reviewing internal data sources for potential fraud.A perennially tricky issue, and one that is just getting thornier, concerns how to treat sensitive, private data in a healthcare context. Healthcare organizations need to be mindful not only of carefully managing protected health information (PHI) subject to the Health Insurance Portability and Accountability Act (HIPAA), but also protected consumer information, which is now subject to regulations such as the California Consumer Privacy Act. Challenges and costs related to being compliant with these regulations are growing and setting themselves up to be just as substantial as managing privilege in litigation.Healthcare data: What privacy rules apply?To make sure these new compliance requirements do not inadvertently extend timelines or burn through budgets, those managing healthcare fraud matters need to proactively take stock of which regulatory regimes concerning personal data are applicable in their case and what data sets being reviewed in their matter could potentially have personal data subject to regulation.Now there is certainly a gray area in distinguishing between protected health information and protected consumer information in a healthcare context. Technically, information is PHI (and therefore subject to HIPAA) if it is created or received by a healthcare provider or health plan. But in today’s data-driven environment, there are a variety of touchpoints between consumers and healthcare services (e.g., marketing data analytics, customer service records, fitness app logs, fringe benefit tracking) that defy traditional understandings of what exactly differentiates PHI from a broader pool of potentially protected consumer data.So, whether subject to HIPAA or CCPA or other privacy mandates, healthcare companies nowadays need to be able to track potentially protected information across all of their data sources, including those not traditionally considered sensitive in that they do not contain information such as health histories, lab test results, or medical bill information.Healthcare fraud: Muddying the data privacy watersThe nature of healthcare fraud further complicates an approach to identifying and appropriately treating sensitive personal data. Matters related to false claims, physician self-referral, Medicaid/Medicare fraud, improper kick-backs, or non-compliant contract and billing practices (to name a few), most often require delving into internal email communications to understand to what extent a fraudulent pattern exists within the organization under investigation, thus enlarging the data pool subject to privacy mandates.The internal work of sorting out billing and coding issues is a messy affair that involves relaying a variety of details of specific patient treatment across multiple related emails. Methodically tracking how these questions get resolved internally over time is at the heart of good healthcare fraud investigation and litigation practice. And carefully treating the sensitive data involved in these conversations is a responsibility that comes with it. If, for instance, you are relying on techniques to extract personal data that have only been tested on structured electronic medical records, you will be missing data that is potentially protected in relevant email discussions.Similar to the task of finding potentially privileged information in large document sets, identifying and treating personal data in healthcare fraud requires its own dedicated workflow, leveraging a mix of tools and methods. The key to successful identification and treatment of protected personal data is being deliberate about the process you design and implement, and specific about the tools you are integrating into it.data-privacyblog, data-privacybloglighthouse

On July 9, 2021, President Biden signed a sweeping new Executive Order (“the Order”) with the stated goal of increasing competition in American markets. Like the recently issued Executive Order on Improving the Nation’s Cybersecurity, the Executive Order on Promoting Competition in the American Economy is meant to establish “a whole-of-government” approach to tackle an issue that is typically handled by numerous federal agencies. As such, the Order includes 72 initiatives touching more than a dozen federal agencies and numerous industries, including healthcare, transportation, agriculture, internet service providers, technology, beer and wine manufacturing, and banking and consumer finance.Notably, the Order calls on the Department of Justice (DOJ) and Federal Trade Commission (FTC) to “vigorously” enforce antitrust laws and “reaffirms” the government’s authority to challenge past transactions that may have been in violation of antitrust laws and regulations (even if they were not challenged by previous Administrations). The remainder of this blog will broadly outline the contents of the Order and conclude with a brief summary on possible ramifications for organizations undergoing merger and acquisition activity (as well as the law firms that counsel them) and how to prepare for them.What is in the Executive Order on Promoting Competition in the American EconomySection 1: PolicyThis section broadly outlines the benefits of “robust competition” to America’s economy and asserts the U.S policy of promoting “competition and innovation” as an answer to the rise of foreign monopolies and cartels. This section also announces the Administration’s policy of supporting “aggressive legislative reforms” to lower prescription drug prices and supports the enactment of a public health insurance option.Sec. 2: The Statutory Basis of a Whole-of-Government Competition Policy This section outlines the antitrust laws which form the Administration’s whole-of-government anti-competition policy, including the Sherman Act, the Clayton Act, and the Federal Trade Commission Act, as well as fair competition and anti-monopolization laws, including Packers and Stockyards Act, Federal Alcohol Administration Act, the Bank Merger Act, and others.Sect 3: Agency Cooperation in Oversight, Investigation, and RemediesThis section outlines the Administration’s policy of cooperation between agencies on anti-competition issues, stating that when there is overlapping jurisdiction over anticompetitive conduct and mergers, the involved agencies should “endeavor to cooperate fully in the exercise of their oversight authority” to benefit from the respective expertise of the agencies and to improve Government efficiency.Section 4: The White House Competition Council This section establishes a White House Competition Council to “coordinate, promote, and advance” government efforts to address monopolies and unfair competition. The section also mandates that the Council should work across agencies to provide a coordinated response to monopolization and unfair competition and outlines the Council make up and meeting cadence.Section 5: Further Agency Responsibilities This section mandates that the heads of all agencies must “consider using their authorities” to further the anti-competition policies outlined within the Order, and “encourages” relevant positions and heads of agencies (including the Attorney General, Chair of the Federal Trade Commission (FTC), Secretary of Commerce, and others) to enforce existing antitrust laws “vigorously,” as well as review and consider revisions to other laws and powers, including encouragement to:Enforce the Clayton Act and other antitrust laws “fairly and vigorously.Review merger guidelines to consider whether they should be revised.Revise positions on the intersection of intellectual property and antitrust laws.Review current practices and adopt a plan for the revitalization of merger oversight under the Bank Merger Act and the Bank Holding Company Act of 1956.Consider whether to revise the Antitrust Guidance for Human Resource Professionals of October 2016.Consider curtailing the unfair use of non-compete clauses that may unfairly limit worker mobility.Consider rulemaking in other areas such as: Unfair data collection and surveillance practices that may damage competition, consumer autonomy, and consumer privacy; Unfair anticompetitive restrictions on third-party repair or self-repair of items (aimed at restrictions that prevent farmers from repairing their own equipment);Unfair anticompetitive conduct or agreements in the prescription drug industries;Unfair competition in major Internet marketplaces;Unfair occupational licensing restrictions;Unfair exclusionary practices in the brokerage or listing of real estate; andAny other unfair industry-specific practices that substantially inhibit competition.The section also calls upon the Secretary of Agriculture to address the unfair treatment of farmers and improve competition in the markets for farm products, and for the Secretary of the Treasury to assess the conditions of competition around the American markets for beer, wine, and spirits (including improving the market for smaller, independent operations).Notably, this section also calls for the Chair of the Federal Communications Commission to consider adopting “Net Neutrality” rules and other avenues to promote competition and lower prices across the telecommunications ecosystem.Finally, the section also calls for the Secretary of Transportation to protect consumers and improve competition in the aviation industry, including enhancing consumer access to airline flight information, providing consumers with more flight options at better prices, promoting rulemaking around requiring airlines to refund baggage fees, and address the failure of airlines to provide timely refunds for flight cancellations resulting from the COVID-10 pandemic.ConclusionAs a whole, the result of this Order will be that organizations undergoing mergers and acquisition activity can expect to face more scrutiny from the government – and that law firms that provide counsel for those types of transactions can expect that government investigations of those activities (like HSR Second Requests) will be more in-depth and meticulous. Accordingly, any law firms and organizations preparing for those types of investigations would do well to evaluate their eDiscovery technology now, in order to ensure that they are using the best and most up-to-date legal technology and workflows to help locate the data requested by the government more accurately and efficiently.antitrustprism, blog, antitrustprism; blog; antitrustsarah moran

Generative AI. Bard. Bing AI. Large language models. Artificial intelligence continues to dominate headlines and workplace chats across every industry since OpenAI’s public release of ChatGPT in November of 2022. Nowhere was this more evident than at this year’s Legalweek event. The annual conference, which gathers thousands of attorneys, legal practitioners, and eDiscovery providers together in New York City, was dominated by discussions of ChatGPT and AI. This makes sense, of course. Attorneys must understand how major technology shifts will impact their clients or companies—especially those in eDiscovery and information governance who deal with corporate data and its challenges. But there was a slight twist to the discussions about ChatGPT. In addition to the possible impacts and risks to clients who use the technology, there was just as much, if not more, focus on how it could be used to streamline eDiscovery.The idea of using a tool released to the public less than four months ago seems almost ironic in an industry with a reputation for slowly adopting technology. Indeed, a 2022 ABA survey showed that as few as 19.2% of lawyers use predictive coding technology (i.e., technology assisted review or TAR) for document review, up from just 12% in 2018. Even surveys dominated by eDiscovery software providers showed TAR was being used on less than 30% of matters in 2022. Given that the technology behind traditional TAR tools has existed since the 1970s and the use of TAR has been widely accepted (and even encouraged) by court systems around the world for over a decade, these statistics are strikingly low.So, what is driving this recent enthusiasm in the industry around AI? The accessibility and generative results of ChatGPT is certainly a factor. After all, even a child can quickly learn how to use ChatGPT to generate new content from a simple query. But the recent excitement in eDiscovery also seems to be driven by the significant challenges attorneys are encountering:Macroeconomic volatility and unpredictability have been a near constant stressor for both corporate legal departments and law firms. Legal budgets are shrinking, and layoffs have plagued almost every industry, leaving legal teams to do the same volume of work with fewer resources.Corporate legal teams are being pressured to evolve from a cost center to one that generates revenue and savings, while attorneys at law firms are expected to add value and expertise to all sectors of a company’s business, beyond the litigation and legal sectors they’ve traditionally operated in. And all attorneys are facing increasing demand to become experts in the risks and challenges of the ever-evolving list of new technology used by their clients and companies.New technology is generating unprecedented volumes of corporate data in new formats, while eDiscovery teams are still grappling with better ways to collect, review, and produce older data formats (modern attachments, collaboration data, text messages, etc.) In short, even the most technology-shy attorneys may be finding themselves at a technology “tipping point,” realizing that it is impossible to overcome some of these challenges without leveraging AI and other forms of advanced technology. But while the challenges may seem grim, there is an inherent hopefulness in this moment. The legal industry’s tendency to adopt AI technology more slowly than other sectors means there’s ample opportunity for growth. Some forward-thinking legal teams, with the help of eDiscovery providers, have already been leveraging advanced AI technology to substantially increase the efficiency and accuracy of eDiscovery workflows. This includes tools that utilize the technology behind ChatGPT, including large language models and natural language processing (NLP). And unlike ChatGPT, where privacy concerns have already been flagged regarding its use, existing AI solutions for eDiscovery were developed specifically to meet the stricter requirements of the legal industry—with some already overcoming tough scrutiny from regulators, opposing counsel, and courts. In other words, the big eDiscovery question of 2023 may not be, “Can ChatGPT revolutionize eDiscovery in the future?” but rather, “What can advanced AI and analytic tools do for eDiscovery practitioners right now?” While the former is up for debate, there are definitive answers to the latter threaded throughout many of the other major industry discussions happening now. Some of those discussions include:If you want to go far, go together Today’s larger and more complicated data volumes often make the traditional eDiscovery model feel like the proverbial round hole that the square (data) peg was not designed to fit into. And it’s becoming increasingly expensive for legal teams to try to do so. To operate in this new era, it’s essential to work with partners who can help you meet your data needs and align with your goals. A good example is when an in-house legal team partners with a technology-forward law firm and eDiscovery provider to build a more streamlined and modern eDiscovery program. This kind of partnership provides the resources, expertise, and technology needed to take a more holistic approach to eDiscovery—breaking away from the traditional model of starting each new matter from scratch. These teams can work together to create and deploy tools and expertise that reduce costs and improve review outcomes across all matters. For example, customized AI classifiers built with data and work product from the company’s past matters, cross-matter analytics that identify review and data trends, and tailored review workflows to increase efficiency and accuracy for specific use cases. This partnership approach is a microcosm of how different organizations and teams can work together to overcome common industry challenges. Technology that meets us where we areDespite all the chatter around ChatGPT, there is currently no “easy AI button” to automate the document review process. However, modern eDiscovery technology (including advanced AI) can be integrated into almost every stage of the document review process in different ways, depending on a case team’s goals. This technology-integrated approach to eDiscovery workflows can help case teams achieve unprecedented efficiency and review accuracy, mitigate risks of inadvertently producing sensitive documents, minimize review redundancy across matters, and quickly pull out key themes, timelines, and documents hidden within large data volumes. Technology-forward law firms and managed review partners can help case teams integrate advanced technology and specialized expertise to achieve these goals in a defensible way that works with each company’s existing data and workflows. The only constant is change The days of a static, rarely updated information governance program are gone. The nature of cloud data, the speed of technology evolution and adoption, and the increasingly complex patchwork of data privacy and security regulations mean that legal and compliance teams need to be nimble and ready for the next new data challenge. New generative AI tools like ChatGPT may only add to this complexity. While this type of technology may be largely off limits in the near future for eDiscovery providers and law firms due to client confidentiality, data privacy, and AI transparency issues, companies in other industries have already begun using it. Legal and compliance teams will need to ensure that any new data created by generative AI tools follow applicable data retention guidelines and regulations and begin to think through how this new data will impact eDiscovery workflows.The furor and excitement over the potential use cases for ChatGPT in eDiscovery are a hopeful sign that more legal practitioners are realizing the potential of AI and advanced analytic technology. This change will help push the industry forward, as more in-house teams, outside counsel, and eDiscovery providers partner together to overcome some of the industry’s toughest data challenges with advanced technology.For other stories on practical applications of AI and analytics in eDiscovery, check out more Lighthouse content. lighting-the-way-for-review; ai-and-analytics; lighting-the-path-to-better-reviewai-big-data, blog, ai-and-analytics,ai-big-data; blogsarah moran

Cloud computing dates to the mid-1990s – so why is this relatively old concept still such a hot topic? Haven’t we figured it all out by now? And isn’t the benefit of today’s SaaS cloud environments that someone else, namely the SaaS provider, handles software management? What else is there to figure out? Having spent the last several months talking to legal, compliance, and IT professionals about their Microsoft 365 environments, I am confident that there is still a lot that corporate IT departments are grappling with. In fact, a recent survey conducted by Lighthouse of 106 IT managers and executives found that although most organizations had a change management process in place for on-premises feature updates and upgrades, and most organizations planned to have change management in place for enterprise-wide SaaS technology updates in the next five years, only 16% had something in place today.[1] To better harness this technology as it continues to evolve and to minimize risks along the way, it’s important to understand why these change management gaps exist, what their impact is, and how legal and IT teams can work together in new ways to close them.Managing the Evolution of SaaSThe adoption of enterprise SaaS cloud technologies has only become prevalent in the last decade and growth has skyrocketed over the last couple of years. In fact, Microsoft 365 had 23.1 million consumer subscribers five years ago (Fiscal Year 2016) and that number has grown to 58.4 million. As such, IT organizations have not had to support SaaS enterprise offerings at scale until very recently and today most IT departments are supporting both on-premises and SaaS cloud environments. The first priority in supporting this explosive adoption was to implement and migrate over to the new system. It is only recently that focus has shifted toward governance and processes around these systems.Even with a newer focus on process, one of the touted benefits of SaaS cloud technology is less maintenance and software support by the in-house IT team. Of course, there is the need to set up process to resolve user questions and to ensure systems have been set up to facilitate the business running properly. But, planning and executing hardware or software upgrades is mostly managed by a third-party provider so there is not an urgent need to set up robust change management. In addition, the old change management process where major developments are analyzed, tested, and timed for deployment to desktops still applies to Microsoft 365.However, using the old process for new applications can have drawbacks. First, not all updates that Microsoft or others make are configurable updates where there is a choice on how, and whether, to implement. Second, if users are logging into a web environment (as opposed to desktop apps), IT teams don’t necessarily have control over the version their users are utilizing. Finally, given that most organizations have differing levels of IT permissions, meaning some groups are upgraded sooner than others, teams must move quickly to handle unpredictable and varied update schedules. With the speed and variability of new feature updates, the old process may not be agile enough to handle them. The differences between SaaS and on-premises environments (where you have full control of the upgrade schedule) can leave some gaps even when organizations review, analyze, and test the roadmap and updates from the Microsoft Message center.The old process often fails to prepare the business for these changes because IT, legal, and other teams are not always communicating about the broader risk or implementation implications. Because the IT team is focused on availability and scalability, it often misses how certain changes can introduce business risks outside of their ken. Solely relying on IT professionals to determine the broader impact of updates can mean that business, regulatory, and other risks outside of IT’s awareness are overlooked.Measuring the Impact of UpdatesWhether these management gaps are tolerable is a risk decision that each organization must make—one that can put the user experience in tension with a developed IT process. In discussions with legal, compliance, and information governance professionals that focus on SaaS services, handling the cadence and speed of these updates is a concern that keeps them up at night. But, quickly providing users new features has considerable benefits for the business too. It’s important for IT to prioritize ensuring that users can access their business data and that the business can continue without interruption over cumbersome update management.When weighing these risks and benefits it’s important to fully appreciate their potential impacts. An example of where these priorities conflict is highlighted in a change around Microsoft Teams meeting transcripts. In March 2021, Microsoft made an update that allows for a live transcript of certain Teams meetings. In November 2021, Microsoft expanded that functionality to Teams Channel meetings and upgraded the features of live transcripts to include name attribution to the speaker. This is helpful functionality for users and, given that it is an automatic upgrade, there may be little to do from an IT perspective. From a risk and legal perspective, however, there are a couple of key considerations. First, where is the transcript stored after the meeting and do retention policies apply? Second, is the data subject to ongoing regulatory or litigation requests and how is it accessed? The answers to those questions are complicated by the fact that the location of the data depends on whether a user downloaded the transcript after the meeting. Many IT organizations caught this change by reviewing the Microsoft Message center for updates—and in doing their own testing they determined that disabling the functionality was the best course of action. This was an update with obvious data ramifications that outweighed the potential benefits in a risk assessment from both IT and legal. For updates that are less obvious, IT may not have consulted legal. For updates where the value to users may seem to outweigh the risk, where the risks aren’t initially apparent, or when there are no configuration options—IT may have a more challenging decision to make.Reimagining a Change Management ProcessHaving a cross-functional framework in place to discuss and implement these types of updates is key to managing changes. Many organizations have some sort of accountability in place around updates—an individual or group of people are responsible for reviewing the Microsoft Message center. Although this structure is lower in cost and requires fewer resources, it has a few drawbacks. First, if only IT is involved, you may have only one perspective on the impacts of updates and that can be too narrow to determine the effects on the broader business. Second, many organizations do not have a tracking mechanism to determine what Microsoft updates they have read, evaluated, tested, and taken action against. With dozens of messages, many of which don’t need action, it is easy to lose track of what has been evaluated. Finally, if there isn’t clear accountability with dedicated resources the process can lose legitimacy and fail. Organizations who choose to minimize their business risk do not have to put in place a heavy structure to manage updates. In fact, the process around on-premises software upgrades can easily be adapted to the cloud situation.The single most important thing that an IT team can do for an effective SaaS support practice is to adapt and enforce existing change management and organizational controls. More specifically, IT organizations should consider:Dedicating a resource to track and review changes from service and cloud providers to ensure updates and changes are properly evaluated for risk and business continuity.Relying on a robust change management system with stakeholders throughout the organization to provide clearly articulated approval, risk identification, testing, and risk management.Partnering with your compliance team to ensure adherence to governance frameworks, organizational commitments, and client requirements. The compliance function is trained to manage risk and is uniquely chartered with authority and independence with a company’s governing body.Collaborating with legal. Lawyers are trained to spot issues and manage risk for the entire business. Often times, individual departmental stakeholders are responding to team-level incentives. Legal teams are also learning to adapt their governance structures to evolving cloud solutions.Leveraging the Project Management Office to ensure that stakeholders and risks are identified at the start of any specific project (i.e., measure twice, cut once).One of the most effective ways to get the right stakeholders’ input is to create a Change Approval Board (“CAB”) with subject matter experts from every business group to meet on a periodic basis. The CAB provides a framework that ensures IT has input from across the business while still giving it the opportunity to own and manage the support of the software.One of the benefits of SaaS technologies is the ability to utilize and optimize with the newest features and to take some of the hardware management burden off IT. By putting in place a cross-functional team to review and manage the update process, you can mitigate your organizational risk while allowing users take full advantage of the benefits.[1] In February 2022, Lighthouse surveyed 106 IT managers or above who had Microsoft on-premises and now have Microsoft 365. The survey found that only 16% had implemented a change management process for M365 and 62% of organizations planned to implement one in the next 5 years.microsoft-365; chat-and-collaboration-data; information-governancemicrosoft, cloud-migration, cloud-services, blog, microsoft-365, chat-and-collaboration-data, information-governance,bloglighthouse

Standardizing your eDiscovery program can be a huge benefit to you and your team. With a well-rounded program, you are able to pressure test and layer in repeatable and trackable processes at each stage of the EDRM. This will result in a lower overall cost of eDiscovery and the ability to more accurately forecast spend from matter to matter. Your program will reduce risk, and increase quality, efficiency, and consistency. You will also have the advantage of program-wide metrics and analysis, leading to knowledge that will empower you to make better and more informed litigation and investigation decisions early on, which in turn leads to better outcomes and greater defensibility. Finally, with your program-wide data tracking you will be able to showcase true ROI and other key metrics. It sounds pretty good, right? So, why doesn’t everyone standardize their eDiscovery program? It can be a challenge. There are several hurdles that one may face when trying to socialize and drive the adoption of their program. For example, lack of alignment across key stakeholders and the challenges of trying to build a program while also managing the pressures of ongoing litigation deadlines. You may also have to invest more time and potentially more cost upfront, which can be a resourcing challenge, and you may have to redefine efficiency across multiple teams. Managing expectations across key stakeholders is critical to building a successful program. Change doesn’t happen overnight.How do you go about overcoming these challenges and standardizing your program? I’ve summarized some tips and best practices below for socializing, implementing, and getting your eDiscovery program to be accepted as the standard both within your organization and beyond.Getting StartedTo begin, build one thing at a time. It is important not to bite off more than you can chew. Start with one project, implement it, and carefully review the results. If it is successful, drive adoption internally, and once it is adopted you can get started on the next project or piece of the program. Be sure all of your key stakeholders are involved early on and set up weekly or even monthly strategy sessions with these stakeholders to ensure that everyone has a seat at the table and a voice in program development decisions. Finally, documentation is your single source of truth. Be sure to think about what you are documenting, where you are storing it, when it should be evaluated for updates, and how it will be circulated after these updates are made. More on driving a successful eDiscovery project can be found in this article, Staying on Pointe: Key Lessons eDiscovery Professionals can Learn from Ballet.Ensuring the Right AudienceAs I mentioned above, you need to be sure to involve all key stakeholders when driving the standardization of your eDiscovery program, but how do you make sure you have the right audience? It is different for everyone and will depend on your organization. Typically, I would recommend that you involve your legal operations and finance teams, as well as any other teams with eDiscovery stakeholders. Once you have these folks identified, set up that recurring strategy meeting.Showing ROIWhen it comes to showing ROI you want to be sure to pick what will make an impact within your company. Whether that be risk reduction, cost reduction, efficiency gains, or something else, you want to focus on what matters at your organization. This is where the documentation I mentioned above comes into play. Be sure you are tracking the metrics and results you would like to report on and format them in graphs, charts, and high-level stats that your key stakeholders can take away and share with their teams. Lean on your providers to help you pull metrics and come up with creative ways to display ROI across your program. It is also important to note that your ROI focus may shift over time, so be sure to remain flexible and check-in with leaders on a bi-annual or annual cadence.Socializing & Driving AdoptionSo, you know how to get started, who to involve, and how to show ROI, but how do you socialize and drive adoption? This is the hardest part and will require flexibility. It is important not to design and drop. You have to continue to reiterate the program and processes consistently. Document your processes, track your results, and make sure you build in a regular feedback loop. Ensure you have support from the right people. This can include your internal teams, outside counsel, vendor(s), etc., and can vary depending on your organization. Be open to feedback and revisions as they come along, document those updates, and share them out.To summarize, when looking to standardize and socialize your eDiscovery program, remember to:involve the right folks early on;build one thing at a time;document the processes;show meaningful ROI; andbe open to feedback - a successful program evolves!To discuss this topic further, please feel free to continue the discussion by emailing me at SBarsky-Harlan@lighthouseglobal.com.ediscovery-review; legal-operationsediscovery-process, blog, ediscovery-review, legal-operationsediscovery-process; blogsarah barsky harlan

Identifying attorney-client privilege is one of the most costly and time-consuming processes in eDiscovery. Since the dawn of the workplace email, responding to discovery requests has had legal teams spending countless hours painstakingly searching through millions of documents to pinpoint attorney-client and other privileged information in order to protect it from production to opposing parties. As technology has improved, legal professionals have gained more tools to help in this process, but inevitably, it still often entails costly human review of massive amounts of documents.What if there was a better way? Recently, I had the opportunity to gather a panel of eDiscovery experts to discuss how advances in AI and analytics technology now allow attorneys to identify privilege more efficiently and accurately than previously possible. Below, I have summarized our discussion and outlined how legal teams can leverage advanced AI technology to reinvent the model for detecting attorney-client privilege.Current Methods of Privilege Identification Result in Over IdentificationCurrently, the search for privileged information includes a hodgepodge of different technology and workflows. Unfortunately, none of them are a magic bullet and all have their own drawbacks. Some of these methods include:Privilege Search Terms: The foundational block of most privilege reviews involves using common privilege search terms (“legal,” “attorney,” etc.) and known attorney names to identify documents that may be privileged, and then having a review team painstakingly re-review those documents to see if they do, in fact, contain privileged information.‍Complex Queries or Scripts: This method builds on the search term method by weighting the potential privilege document population into ‘tiers’ for prioritized privilege review. It sometimes uses search term frequency to weigh the perceived risk that a document is privileged.‍Technology Assisted Review (TAR): The latest iteration of privilege identification methodologies involves using the TAR process to try to further rank potential privilege populations for prioritized review, allowing legal teams to cut off review once the statistical likelihood of a document containing privilege information reaches a certain percentage.Even applied together, all these methodologies are only just slightly more accurate than a basic privilege search term application. TAR, for example, may flag 1 out of every 4 documents as privilege, instead of the 1 out of every 5 typically identified by common privilege search term screens. This result means that review teams are still forced to re-review massive amounts of documents for privilege.The current methods tend to over-identify privilege for two very important reasons: (1) they rely on a “bag of words” approach to privilege classification, which removes all context from the communication; (2) they cannot leverage non-text document features, like metadata, to evaluate patterns within the documents that often provide key contextual insights indicating a privileged communication.How Can Advances in AI Technology Improve Privilege Identification MethodsAdvances in AI technology over the last two years can now make privilege classification more effective in a few different ways:Leveraging Past Work Product: Newer technology can pull in and analyze the privilege coding that was applied on previous reviews, without disrupting the current review process. This helps reduce the amount of attorney review needed from the start, as the analytics technology can use this past work product rather than training a model from scratch based on review work in the current matter. Often companies have tens or even hundreds of thousands of prior privilege calls sitting in inactive or archived databases that can be leveraged to train a privilege model. This approach additionally allows legal teams to immediately eliminate documents that were identified as privileged in previous reviews.Analyzing More Than Text: Newer technology is also more effective because it now can analyze more than just the simple text of a document. It can also analyze patterns in metadata and other properties of documents, like participants, participant accounts, and domain names. For example, documents with a large number of participants are much less likely to contain information protected by attorney-client privilege, and newer technology can immediately de-prioritize these documents as needing privilege review.Taking Context into Account: Newer technology also has the ability to perform a more complicated analysis of text through algorithms that can better assess the context of a document. For example, Natural Language Processing (NLP) can much more effectively understand context within documents than methods that focus more on simple term frequency. Analyzing for context is critical in identifying privilege, particularly when an attorney may just be generally discussing business issues vs. when an attorney is specifically providing legal advice.Benefits of Leveraging Advances in AI and Analytics in Privilege ReviewsLeveraging the advances in AI outlined above to identify privilege means that legal teams will have more confidence in the accuracy of their privilege screening and review process. This technology also makes it much easier to assemble privilege logs and apply privilege redactions, not only to increase efficiency and accuracy, but also because of the ability to better analyze metadata and context. This in turn helps with privilege log document descriptions and justifications and ensuring consistency. But, by far the biggest gain, is the ability to significantly reduce costly and time-intensive manual review and re-review required by legal teams using older search terms and TAR methodologies.ConclusionLeveraging advances in AI and analytics technology enables review teams to identify privileged information more accurately and efficiently. This in turn allows for a more consistent work product, more efficient reviews, and ultimately, lower eDiscovery costs.If you’re interested in learning more about AI and analytics advancements, check out my other articles on how this technology can also help detect personal information within large datasets, as well as how to build a business case for AI and win over AI naysayers within your organization.To discuss this topic more or to learn how we can help you make an apples-to-apples comparison, feel free to reach out to me at RHellewell@lighthouseglobal.com.ai-and-analytics; chat-and-collaboration-data; ediscovery-reviewprivilege, analytics, ai-big-data, blog, ai-and-analytics, chat-and-collaboration-data, ediscovery-review,privilege; analytics; ai-big-data; bloglighthouse

Legal department automation may be top of mind for you like several other legal operations professionals, however, you might be dependent on IT or engineering resources to be able to execute. Or perhaps you are struggling with change management and not able to implement something new. You are not alone. These were the top two blockers to building out an efficient process within legal departments as shared by recent CLOC conference attendees. The good news is that off-the-shelf technologies have advanced to the point where you may not need any time from those resources and may be able to manage automation without needing to change user behavior. With “no code” automation, you can execute end-to-end automation for your legal operations department, yourself!What is “No Code” Automation?As recently highlighted in Forbes magazine, “no-code platforms feature prebuilt drag-and-drop activities and tasks that facilitate integration at the business user level.” This is not “low code” automation that has been around for decades. Low code refers to using existing code, whether from open source or from other internal development, to lower the need to create new code. Low code allows you to build faster but still requires the knowledge of code. In “no code,” however, you do not need to have an understanding of coding. What this really means is that no code platforms are so user-friendly that even a lawyer, or legal operations professional, can create automated actions…I know because I am a lawyer that has successfully done this!But, How Does this Apply in Legal Operations?The short answer is that it lets you, the legal operations professional, automate workflows with little external help. There are some legal departments already taking advantage of this technology. At a recent CLOC conference, Google shared how they had leveraged “no code” automation to remove the change management process for ethics and compliance in the code of conduct, conflict of interest, and anti-bribery and corruption areas. With respect to outside counsel management, Google was similarly able to remove IT/engineering dependencies for conflict waiver approvals, outside counsel engagements, and matter creation. For more details, watch Google describe their no-code automation use cases.Google’s workflow automation is impressive and more mature than those of us who are just starting, so I wanted to share a simple example. A commonplace challenge for smaller legal teams is to manage tasks – ensuring all legal requests are captured and assigned to someone on the legal team. Many teams are dealing with dozens, or hundreds, of emails and it can be cumbersome to look through those to determine who is working on what. Inevitably some of those requests get missed. It is also challenging to then later report on legal requests – e.g., what types of requests the legal team receives daily, how long they take to resolve, and how many requests each person can work on. A “no code” platform can help. For example, you can connect your email to a shared Excel spreadsheet that captures all legal tasks. You would do this by creating a process that has the tool log each email sent to a certain address (e.g. legal@insertconame.com) on an Excel spreadsheet in a shared location (e.g. LegalTasks.xls). You would “map” parts of the email to columns in the spreadsheet. For example, you would want to capture the sender, the date, the time, the subject, and the body. You can even ask users who are sending requests into that email to put the type of request in the subject line. Your legal team can then check the shared spreadsheet daily and “check out” tasks by putting their initials in another column. Once complete, they would also mark that on the spreadsheet. Capturing all this information will allow you to see who is working on what, ensure that all requests are being worked on, and use pivot reporting on all legal tasks later on. Although this is a really simple use case with basic tools, it is also one that takes only a few minutes to set up and can measurably improve organization among legal team members.You can use “no code” automation in most areas of legal operations department automation. Some of the most common things to automate with “no code” are as follows:Legal ApprovalsDocument GenerationsEvidence CollectionTracking of Policy AcceptanceMany “no code” companies work with legal departments, so they may have experience with legal operations use cases. Be sure to ask how they have seen their technologies deployed in other legal departments.Can I Really Do This Without Other Departments?About 90% of the work can be done by you or your team, and in some cases, even 100%. However, sometimes connecting the tools or even installing the software has to be done by your IT and development teams. This is particularly true if you are connecting to proprietary software or have a complex infrastructure. This 10% of work required by these teams, however, is much smaller than if you were asking for those resources to create the automations from scratch. In addition, you often do not have to change user behavior so change management is removed as a blocker.I encourage you to explore using “no code” automation in your legal department. Once you start, you’ll be glad you tried. I would be excited to hear your experiences with “no code” in legal operations. If you are using it, drop me a line at djones@lighthouseglobal.com and tell me how.legal-operations; ediscovery-reviewediscovery-process, legal-ops, blog, legal-operations, ediscovery-reviewediscovery-process; legal-ops; bloglighthouse

Below is a copy of a featured article written by Jennifer Swanton of Medtronic, Shannon Capone Kirk of Ropes & Gray, and John Del Piero of Lighthouse for Legaltech News.This is the second article in a two-part series, designed to help create a better relationship between corporate attorneys and advanced technology. In our first article, we worked to demystify the language technology providers tend to use around AI and analytics technology.With the terminology now defined, we will now focus on six specific ways that corporate legal teams can put this type of technology to work in the eDiscovery and compliance space to improve cost, outcome, efficiencies.1. Document Review and Data Prioritization: The earliest example of how to maximize the value of analytics in eDiscovery was the introduction of TAR (technology-assisted review). CAL (or continuous active learning) allows counsel to see the most likely to be relevant documents much earlier on in the process than if they had been simply looking at search term results, which are not categorized or prioritized and are often overbroad. Plainly put, it is the difference between an organized review and a disorganized review.Data prioritization offers strategic value to the case team, enabling them to get to the crux of a case earlier in the process and ultimately develop a better strategic plan for cost and outcomes. This process also offers the ability to get to a point of review where the likelihood of additional relevant information is so low, no new review is needed. This will save time and money on large document review projects. Such prioritization is critical for time-sensitive internal investigations, as well.To dive further into the Pandora analogy we used above: if you were to listen to a random shuffle of songs on Pandora without giving feedback on what you like and don’t like, you’d likely listen for days to encounter several songs you love. Whereas, if you give Pandora feedback, it learns and you’re likely to hear several songs you love within hours. So why suffer days of listening to show tunes and harp solos when what you really love is the brilliant artistry found in songs by the likes of Ray LaMontagne?2. Custodian and Data Source Identification: Advanced analytics that can analyze complex concepts within data can be a powerful tool to clearly identify your relevant data custodians, where that data lives, and other data sources worth considering. Most conceptual analytics technology can now provide real-time visibility into information about custodians, including the date range of the data collected and the data types delivered. More advanced technology that also analyzes metadata can provide you with a deeper understanding of how custodians interact with other people, including the ability to analyze patterns in timing and speech, and even the sentiment and tone of those interactions.All of this information can be used to help quickly determine whether or not a prospective custodian has information relevant to the case that needs to be collected, or if any supplemental collections are required to close a gap in the date range collected. This, in turn, will help reduce the amount of collections required and minimize processing time in fast-paced cases. These tools also help determine which data sources are likely to hold your most relevant information and where supplemental collections may be warranted.Above: Brainspace display of communication networks, which enable users to identify custodians of interest, as well as related people and conversations.3. Identifying Privileged and Personal Information: Another powerful way to leverage analytics in the eDiscovery workflow is to identify privileged documents in a far more cost-effective way than we could in the past. New privilege categorization software creates significant efficiencies by analyzing the text, metadata, and previous coding of documents in order to categorize documents according to the likelihood that they are actually privileged.More advanced analytics tools can now identify documents that have been flagged as privileged by traditional privilege term screens, but have a high likelihood of not containing privileged communications. For example, the technology identifies that the document was sent to a third-party (thus breaking the privilege attorney-client privilege) or because the only privilege term within the document is contained within a boilerplate footer.These more advanced analytics tools can be much more effective at identifying privileged documents than a privilege search term list, and can help case teams successfully meet rolling production deadlines by pushing the documents that are less likely to be privileged (i.e. those that require less privilege review) to the front of the review line. When integrated with other eDiscovery applications, you can also create a defensible privilege log that can be produced for the litigation team.Additionally, flagging potential PII and protected intellectual property (IP) caught up in a large data set can be challenging, but analytics technology provides in-house legal teams with an important ally for automating those processes. Advanced analytics can streamline the process of locating and isolating this sensitive data, which is often hiding in a variety of different systems, folders, and other information silos. Tools allow you to flag Health Insurance Portability and Accountability Act (HIPAA) protected information based on common format and structure to help quickly move through documents and accurately identify and redact needed information.4. Information Governance: One of the high-stakes elements of large data collections is the importance of parsing out highly sensitive records, such as those that contain PII and protected IP. This information is incredibly important to protect company data and also to comply with the growing number of data privacy regulations worldwide, including Europe’s General Data Protection Regulation (GDPR), the California Consumer Protection Act (CCPA), and HIPAA. Analytics can help identify and flag documents per their appropriate document classification. This can be helpful for both the business in their day-to-day operations as well as the legal team in responding to requests.5. Data Re-Use: One of the largest potentials with the use of analytics is the ability to save time and money on your next matter. Technologically advanced companies are now starting to use analytics technology to integrate previous attorney work product, case information, and documents across all organization matters. On a micro level, recycling and analyzing previous work product allows companies to stop re-inventing the wheel on each case and aids in much faster identification of privilege, personal information, and non-responsive documents.For example, organizations often pay to store documents that contain previous privilege tagging from past matters in inactive or archived databases. Those documents, sitting unused in storage, can be separately re-ingested and used to train a privilege model in the new matter, allowing legal teams to immediately eliminate documents that were identified as privileged in previous reviews—even prior to any human coding in the new matter.On a macro level, this type of advanced capability enables organizations to make data-driven decisions across their entire eDiscovery landscape. Rather than looking at each new matter on an individual basis in a singular lens, legal teams can use advanced analytics to analyze previously coded data across the organization’s entire legal portfolio. This can provide previously unheard of insights, like which custodians often contain the most privileged documents matter over matter, or if a data source rarely produces responsive documents. Data re-use can also come in handy in portfolio matters that have overlapping custodians and data sets and need common production. The overall results are more strategic legal and data decisions, more favorable case outcomes, and increased cost efficiency.6. Accuracy: Finally, and potentially the most important reason to use analytics tools, is to increase accuracy and have a better work product. Studies have shown that tools like predictive coding are more accurate than human work product. That, coupled with the potential for cost savings, should be all one needs to utilize these technologies.As useful as these new analytics tools are to in-house legal teams in their efforts to manage eDiscovery today, it is important to understand that the great promise of these technologies is the fact that they are in a state of continuous improvement. Because analytics tools learn, they refine and “get smarter” as they review more data sets. We all know that we’re on just the cusp of what analytics will bring to our profession—but we believe the future of this technology in the area of eDiscovery management is here now.ai-and-analyticstar-predictive-coding, blog, corporate, ai, ai-and-analytics,tar-predictive-coding; blog; corporate; aijohn del piero

As the corporate workplace continues to evolve—encompassing hybrid work environments, bring your own device policies, and cloud-based storage—companies are well-advised to consider areas of increased vulnerability and whether their policies, procedures, and forensic tools are keeping pace with reality. A hybrid or remote workforce and a more collaborative data infrastructure only exacerbate data risks that were easier to manage when employees were comfortably situated at their desks. Adding even more complexity to these risks are broader labor trends, including “the Great Resignation and Reshuffle” and an aging work force, which are changing staffing and recruiting strategies and impacting knowledge transfer and IP creation.Employee intake and departure: crucial points of data security Two areas likely needing renewed attention are the moments of employee onboarding and offboarding, when a company’s most prized assets—people and data—are on the move. Departing employees present a particular risk as the potential for data exfiltration of IP and other sensitive information, whether intentional or not, is high. Often, employees take corporate IP with them inadvertently, a situation bound to get worse as turnover rates grow (Gartner anticipates a 20% jump in turnover from the pre-pandemic national average).Since people usually take jobs similar to the ones they leave (and often with competitors), taking company data along with their coffee mug and potted plant may seem justified (I wrote this stuff, so it’s mine)—or simply inconsequential. Cloud storage services such as Dropbox, Box, or Google Drive, and collaborative apps such as Microsoft Teams or Slack make it all the easier to appropriate files, lending credence to a feeling of personal data ownership. No matter how it happens, the escape into the wild of proprietary items such as source code, strategy documents, contact lists, and financial information exposes the company to untold risk, including the danger of running afoul of any number of privacy regulations if personal data is exfiltrated from its protected environment—an additional headache for the company if things go south. Are current entry and exit protocols enough? Although most companies have entrance and exit protocols usually siloed as HR and IT functions, the recent surge in employee turnover has put those very teams under pressure as they face their own personnel and budget deficits. Further, responsibilities have become less defined at a time when offboarding tasks—many now carried out at a distance—should be fortified to include proactive data monitoring and oversight, activities such teams may not be equipped to handle. The challenge, of course, is the growing complexity of the data landscape. Knowing what information is where, who accesses it, and for what purpose becomes more difficult to track as software and storage options grow, yet this is key to keeping important data protected. Data security: start training early and reinforce often Onboarding procedures can play a key role in keeping data where it belongs and helping employees navigate through and understand their responsibilities in this increasingly intricate data terrain. First, a sound onboarding protocol can ensure that new employees aren’t bringing troublesome data into the environment. No company wants to deal with the fallout of being in possession of some other company’s IP or sensitive information. More importantly, onboarding offers the most opportune time to clearly communicate expectations regarding data management and safety—information that should be reinforced with frequent (and up to date) training that emphasizes data protection and ownership. It's easy to forget as time goes on what data may be confidential or sensitive, and even easier to forget that data belongs to the business, not the employee. In short, data awareness should be instilled as part of the company culture right from the start. Seize the moment: identify and monitor offboarding risksThe recent and ongoing workplace disruption calls for a hard look at offboarding data risks and an evaluation of potential vulnerabilities to protect data before an employee leaves the company, bolster the exit protocols to have in place when they do, and have the proper forensic and analytic tools to handle data monitoring and address potential wrongdoing. Most companies do have standard offboarding checklists that address employee assets, data access, and preservation obligations as they leave the company. But there’s more to data protection at this crucial moment than ticking off boxes. Expand and optimize the offboarding checklistSavvy companies implement a more proactive, programmatic approach that begins earlier, with monitoring procedures that include defensible and repeatable processes to guard against the exfiltration of company data while helping to fortify the company’s position in case of a breach. A few important things to consider as part of the offboarding process:Know which employees warrant departure attention. Develop risk profiles with business stakeholders to identify which classes of employees, whether based on role, circumstance of departure, seniority, or access to sensitive information could present an exfiltration risk.Understand the company’s data landscape. Make sure there are mechanisms in place for tracking where sensitive data and IP may reside and when such data has been accessed.Explore activity and assets with the employee prior to their departure. An expert, friendly review of a departing employee’s recent computer activity with the employee, including an audit of their recent network activities, use of peripherals, cloud uploads, and email sends, can reveal and help mitigate potential trouble.Preserve employee devices and data as warranted with state-of-the-art forensic tools. Forensic preservation is critical to ensuring valid evidence down the line, especially since investigations today regularly involve new and novel devices, data sources, and artifacts that must be diagnosed and understood.Document all offboarding information. A paper trail of findings during the exit procedure is important if further analysis is recommended or necessary and will be crucial for subsequent investigation, if it comes to that. Have a plan if there is evidence of wrongdoing. Part of any data security effort is having an action plan to execute if there are signs of a breach. Preservation, collection, and a forensic analysis may be required should legal action ensue. ConclusionThe recent upheaval in employee turnover along with more collaboration tools and storage options present increasing risk for today’s enterprise. Companies that acknowledge new vulnerabilities and leverage opportunities to revamp outdated policies and protocols are better positioned to stop data exfiltration before it becomes a problem. The best solution: Implement robust onboarding and offboarding solutions that include data monitoring, reporting, and forensic analysis to enable a quick pivot to actionable remediation steps if trouble is brewing. digital-forensics; information-governancedeparting-onboarding-employee, blog, risk-management, digital-forensics, information-governancedeparting-onboarding-employee; blog; risk-managementdaniel black

Increasingly, opportunities for cloud-based collaboration and efficiencies, and challenges presented by the rapid proliferation of complex data, are incentivizing organizations to transform their corporate data governance and eDiscovery operations from traditional self-managed infrastructure to the Microsoft 365 (M365) Cloud. Benefits in terms of convenience, security, robust functionality, and native capabilities related to eDiscovery and compliance are the primary drivers of this move.While there are many benefits to moving into the M365 ecosystem, it requires legal and compliance teams to take on new considerations regarding the constant evolution that characterizes cloud software. With continually changing applications, establishing static workflows for eDiscovery, legal holds, data dispositions, and other legal operations is not enough. As the M365 software and functionality changes, workflows must be constantly evaluated to ensure their validity, relevance, and defensibility.Exacerbating this challenge is the reality that the traditional IT change management paradigm designed to preemptively address cross-organizational considerations (including impacts to legal, compliance, and eDiscovery operations) does not fit the Cloud/SaaS framework. Organizations must now rethink their change management approach as they modernize with M365.This is the first in a series of blog posts devoted to highlighting key changes that have been released into the M365 production environments. One of the biggest challenges for organizations is identifying which of the myriad of updates pose potential risks to eDiscovery operations. Distinguishing the changes that do and do not pose a significant eDiscovery impact can be extremely difficult unless the reviewer has some level of subject-matter expertise and understands the specific workflows deployed within the organization. Here are some common scenarios with potential eDiscovery impact that could easily go unnoticed by the untrained eye:Updates that create a new data sourceUpdates that change a backend data storage locationUpdates altering the risk profile of features that were previously disabled due to legal / privacy riskUpdates that render an existing eDiscovery process obsoleteEach subsequent blog post in this series will highlight an example of a software update related to our key software scenarios, detailing the nature of the change, the potential impact, as well as when and why organizations should care.microsoft-365; chat-and-collaboration-data; information-governancemicrosoft, compliance-and-investigations, blog, cloudcompass, advisory-services, microsoft-365, chat-and-collaboration-data, information-governance,microsoft; compliance-and-investigations; blog; cloudcompass; advisory-serviceslighthouse

Below is a copy of a featured article written by Jennifer Swanton of Medtronic, Shannon Capone Kirk of Ropes & Gray, and John Del Piero of Lighthouse for Legaltech News.Despite the traditional narrative that lawyers are hesitant to embrace technology, many in-house legal departments and their outside service providers are embracing the use of what is generally referred to as artificial intelligence (AI). In terms of litigation and internal investigations, this translates more specifically into conceptual analytics and predictive coding (also referred to as continuous active learning, or CAL), which are two of the more advanced technological innovations in the litigation space and corporate America.This adoption, in part, seems to be driven by an expectation from corporate leaders that their in-house counsel must be able to identify and utilize the best available technology in order to drive cost efficiency, while also reducing risk and increasing effective and defensible litigation positions. For instance, in a 2019 survey of 163 legal professionals conducted by ALM Intelligence and LexisNexis, 92% of attorneys surveyed planned to increase their use of legal analytics in the upcoming 12 months. The reasoning behind that expected increase in adoption was two-fold, with lawyers indicating that it was driven both by competitive pressure to win cases (57%), as well as client expectation (56%).Given that the above survey took place right before the COVID-19 pandemic hit, it stands to reason that the 92% of attorneys that expected to increase their use of analytics tools in 2020 may actually be even higher now. With a divisive election and receding pandemic only recently behind us, and an already unpredictable market, many corporations are tightening budgets and looking to further reduce unnecessary spend. Conceptual analytics and CAL are easy (yes, really) and effective ways to manage ballooning datasets and significantly reduce discovery, litigation and internal investigation costs.With that in mind, we would like to help create a better relationship between corporate attorneys and advanced technology with the following two step approach—which we will outline in a series of two articles.This first installment will help demystify the language technology providers tend to use around AI and analytics technology so that in-house teams feel more comfortable with adoption. In our second article, we will provide examples of some great use cases where corporate legal teams can easily leverage technology to help improve workflows. Together, we hope this approach can help in-house legal teams adopt technology that drives efficiency, lowers cost, and improves the quality of their work.Demystifying AI JargonIf you have ever discussed AI or analytics technology with a technology provider, you are probably more than aware that tech folks have a tendency to forget that the majority of their clients don’t live in the world of developing and evaluating new technology, day in and day out. Thus, they may use terms that are often confusing to their legal counterparts (and sometimes use terms that don’t match what the technology is capable of in the legal world). For this reason, it is helpful to level set with some common terminology and definitions, so that in-house attorneys are prepared to have better, more practical real-world discussions with technology providers.Analytics Technology: Within the eDiscovery and compliance space, analytics technology is the ability of a machine to recognize patterns, structures, concepts, terminology, and/or the people interacting within data, and then present that analysis in a visual representation so that attorneys have a better overview of their data. As with AI, not all analytics tools have the same capabilities. Vendors may label everything from email threading identification to more advanced technology that can identify complex concepts and human sentiment as “analytics” tools.Within these articles, when we reference this term, we are referring to the more advanced technology that can analyze not only the text within data but also the metadata and any previous coding applied by subject matter experts. This is an important distinction because this type of technology can greatly improve the accuracy of the analysis compared to older tools. For example, analytics technology that can analyze metadata as well as text is much better at identifying concepts like attorney-client privilege because it can analyze not only the language being used but who is using that language and the circumstances in which they use it.Artificial Intelligence (AI): Probably the most broadly recognized term due to its prevalence outside of the eDiscovery space, AI is technically defined as the ability of a computer to complete tasks that usually would require human intelligence. Within the eDiscovery and compliance world, vendors often use the term broadly to refer to a variety of technologies that can perform tasks that previously would require completely human review.It is important to remember though that the term AI can refer to a broad range of technology with very different capabilities. “AI” in the legal world is currently being used as a generalized term and legal consumers of such technologies should press for specifics—not all “AI” is the same, or, in several cases, even AI at all.Machine Learning: Machine learning is a category of algorithms used in AI that can analyze statistics and find patterns in large volumes of data. The algorithms improve with experience—meaning that as documents are coded in a consistent fashion by humans, the better and more accurate the algorithms should become at identifying specific data types. Note here that there is a common misunderstanding that machine learning requires large amounts of data from which to learn. That is not necessarily true—all that is required for machine learning to work well is that the input it learns from (i.e., document coding for eDiscovery purposes) is consistent and accurate.Natural Language Processing (NLP): NLP is a subset of AI that uses machine learning to process and analyze the natural language humans use within large amounts of data. The result is technology that can “understand” the contents of documents, including the context in which language is used within them. Within eDiscovery, NLP is used within more advanced forms of analytics technology to help identify specific content or sentiments within large datasets.For example, NLP can be used to more accurately identify sensitive information, like personally identifiable information (PII), within datasets. NLP is better at this task than older AI technology because older models relied on “regular expressions” (a sequence of characters to define a search pattern) to identify information. When a “regular expression” (or regex) is used by an algorithm to find, for example, VISA account numbers—it will be able to identify the correct number pattern (i.e., any number that starts with the number 4 and has 16 digits) within a dataset but will be unable to differentiate other numbers that have the same pattern (for example, employee identification numbers). Thus, the results returned by legacy technology using regex may be overbroad and include false positives.NLP can return more accurate results for that same task because it is able to identify not only the number pattern, but can also analyze the language used around the pattern. In this way, NLP will understand the context in which VISA account numbers are communicated within that dataset compared to how employee identification numbers are communicated, and only return the VISA numbers.Predictive Coding (also referred to as Technology-Assisted Review or TAR): Predictive coding is not the same as conceptual analytics. Also, predictive coding is a bit of a misnomer, as the tools don’t predict or code anything. A human reviewer is very much involved. Simply put, it refers to a form of machine learning, wherein humans review documents and make binary coding calls: what is responsive and what is non-responsive. This is similar in concept to selecting thumbs up or down in Pandora so as to teach the app what songs you like and don’t like. After some human coding and calibrations between the human and the tool, the technology uses the human’s coding selections to score how the remaining documents should be coded, enabling the human to review the high scored documents first.In the most current versions of predictive coding, this technology continually improves and refreshes as the human reviews, which reduces or eliminates the need for surgical precision on coding at the start (which was a concern in the former version of predictive coding and why providers and parties spent a considerable amount of time concerned with “seed sets”). This improved and self-improving prioritization of large document sets based on high-scored documents is usually a more efficient and organized manner in which to review documents.Because of this evolution in predictive coding, it is often referred to in a host of different ways, such as TAR 1.0 (which requires “seed sets” to learn from at the start) and TAR 2.0 (which is able to continually refresh as the human codes—and is thus also referred to as Continuous Active Learning or CAL). Some providers continue to use the old terminology, or explain their advancements by walking through the differences between TAR 1.0 and TAR 2.0, and so on. But, speaking plainly, in this day and age, providers and legal teams should really only be concerned with the latest version of TAR, which utilizes CAL, and significantly reduces or totally eliminates the previous concern with surgical precision on coding an initial “seed set.” With our examples in the next installment, we hope to illustrate this point. In a word, walking through the technological evolution around predictive coding and all of the associated terminology can cause unnecessary intimidation, and can cause confusion between providers, parties and the court.The key takeaway from these definitions is that even though all the technology described above may technically fall into the “AI” bucket, there is an important distinction between predictive coding/TAR technology and advanced analytics technology that uses AI and NLP. The distinction is that predictive coding/TAR is a much more technologically-limited method of ranking documents based on binary human decisions, while advanced analytics technology is capable of analyzing the context of human language used within documents to accurately identify a wide variety of concepts and sentiment within a dataset. Both tools still require a good amount of interaction with human reviewers and both are not mutually exclusive. In fact, on many investigations in particular, it is often very efficient to employ both conceptual analytics and TAR, simultaneously, in a review.Please stay tuned for our next installment in this series, “Analytics and Predictive Coding Technology for Corporate Attorneys: Six Use Cases”, where we will outline six specific ways that corporate legal teams can put this type of technology to work in the eDiscovery and compliance space to improve cost, outcome, efficiencies.ai-and-analyticstar-predictive-coding, blog, corporate, ai, ai-and-analytics,tar-predictive-coding; blog; corporate; ailegaltech news

Big data sets are the “new normal” of discovery and bring with them six sinister large data set challenges, as recently detailed in my colleague Nick’s article. These challenges range from classics like overly broad privileged screens, to newer risks in ensuring sensitive information (such as personally identifiable information (PII) or proprietary information such as source code) does not inadvertently make its way into the hands of opposing parties or government regulators. While these challenges may seem insurmountable due to ever-increasing data volumes (and also tend to keep discovery program managers and counsel up at night) there are new solutions that can help mitigate these risks and optimize workflows.As I previously wrote, eDiscovery is actually a big data challenge. Advances in AI and machine learning, when applied to eDiscovery big data, can help mitigate and reduce these sinister risks by breaking down the silos of individual cases, learning from a wealth of prior case data, and then transferring these learnings to new cases. Having the capability to analyze and understand large data sets at scale combined with state-of-the-art methods provides a number of benefits, five of which I have outlined below.Pinpointing Sensitive Information - Advances in deep learning and natural language processing has now made pinpointing sensitive content achievable. A company’s most confidential content could be laying in plain sight within their electronic data and yet be completely undetected. Imagine a spreadsheet listing customers, dates of birth, and social security numbers attached to an email between sales reps. What if you are a technology company and two developers are emailing each other snippets of your company’s source code? Now that digital medium is the dominant form of communication within workplaces, situations like this are becoming ever-present and it is very challenging for review teams to effectively identify and triage this content. To solve this challenge, advanced analytics can learn from massive amounts of publically available and computer-generated data and then fine tuned to specific data sets using a recent breakthrough innovation in natural language processing (NLP) called “transfer learning.” In addition, at the core of big data is the capability to process text at scale. Combining these two techniques enables precise algorithms to evaluate massive amounts of discovery data, pinpoint sensitive data elements, and elevate them to review teams for a targeted review workflow.Prioritizing the Right Documents - Advanced analytics can learn both key trends and deep insights about your documents and review criteria. A normal search term based approach to identify potentially responsive or privileged content provides a binary output. Documents either hit on a search term or they do not. Document review workflows are predicated on this concept, often leading to suboptimal review workflows that both over-identify documents that are out of scope and miss documents that should be reviewed. Advanced analytics provide a range of outcomes that enable review teams to create targeted workflow streams tailored to the risk at hand. Descriptive analysis on data can generate human interpretable rules that help organize documents, such as “all documents with more than X number of recipients is never privileged” or “99.9% of the time, documents coming from the following domains are never responsive”. Deep learning-based classifiers, again using transfer learning, can generalize language on open source content and then fine-tune models to specific review data sets. Having a combination of analytics, both descriptive and predictive, provides a range of options and gives review teams the ability to prioritize the right content, rather than just the next random document. Review teams can now concentrate on the most important material while deprioritizing the less important content for a later effort.Achieving Work-Product Consistency - Big data and advanced analytics approaches can ensure the same document or similar documents are treated consistently across cases. Corporations regularly collect, process, and review the same data across cases over and over again, even when cases are not related. Keeping document treatment consistent across these matters can obviously be extremely important when dealing with privilege content – but is also important when it comes to responsiveness across related cases, such as a multi-district litigation. With the standard approach, cases are in siloes without any connectivity between them to enable consistent approaches. A big data approach enables connectivity between cases using hub-and-spoke techniques to communicate and transit learnings and work-product between cases. Work product from other cases, such as coding calls, redactions, and even production information can be utilized to inform workflows on the next case. For big data, activities like this are table stakes.Mitigating Risk - What do all of these approaches have in common? At its core, big data and analytics is an engine for mitigating risk. Having the ability to pinpoint sensitive data, prioritize what you look at, and ensure consistency across your cases is a no-brainer. This all may sound like a big change, but in reality, it’s pretty seamless to implement. Instead of simply batching out documents that hit on an outdated privilege screen for privilege review, review managers can instead use a combination of analytics and fine-tuned privilege screen hits. Review then occurs from there largely as it does today, just with the right analytics to inform reviewers with the context needed to make the best decision.Reducing Cost - The other side of the coin is cost savings. Every case has a different cost and risk profile and advanced analytics should provide a range of options to support your decision making process on where to set the lever. Do you really need to review each of these categories in full, or would an alternative scenario based on sampling high-volume and low-risk documents be a more cost-effective and defensible approach? The point is that having a better and more holistic view of your data provides an opportunity to make these data-driven decisions to reduce costs.One key tip to remember - you do not need to try to implement this all at once! Start by identifying a key area where you want to make improvements, determine how you can measure the current performance of the process, then apply some of these methods and measure the results. Innovation is about getting a win in order to perpetuate the next.If you are interested in this topic or just love to talk about big data and analytics, feel free to reach out to me at KSobylak@lighthouseglobal.com.ai-and-analyticsdigital-forensics, ai-and-analyticsanalytics; ai-big-data; data-re-use; blogkarl sobylak

Recently, I had the pleasure of appearing as a guest on Season 5, Episode 1 of the Law & Candor podcast, hosted by Lighthouse’s Rob Hellewell and Bill Mariano. The three of us discussed cloud migrations and how that process can provide a real opportunity for an organization to transform its approach to information governance. Below is a summary of our conversation, including best practices for organizations that are ready to take on this digital and cultural cloud transformation process.Because it is difficult to wrap your head around the idea of a cloud transformation, it can be helpful to visualize the individual processes involved on a much smaller scale. Imagine you are simply preparing to upgrade to a new computer. Over the years, you have developed bad habits around how you store data on your old computer, in part because the tools on that computer have become outdated. Now that you’re upgrading, you have the opportunity to evaluate your old stored data to identify what is worth moving to your new computer. You also have the opportunity to re-evaluate your data storage practice as a whole and come up with a more efficient plan that utilizes the advanced tools on your new computer. Similarly, the cloud migration process is the best opportunity an organization has to reassess what data should be migrated, how employees interact with that data, and how that data flows through the organization before building a brand new paradigm in the Cloud.You can think of this new paradigm as the organization’s information architecture. Just like a physical architecture where the architect designs a physical space for things, an organization’s information architecture is the infrastructure wherein the organization’s data will reside. To create this architecture effectively, you first must analyze how data flows throughout the company. To visualize this process, imagine the flow of information as a content pipeline: you’ve got a pile of papers and files on your desk that you want to assess, retain what is useful to you, and then pass on to the next person down the pipe. First, you would identify the files you no longer need and discard those. Next, you would identify what files you need for your work and put those aside for yourself. Then you would pass the remaining pile down to the next person in the pipeline, who has a different role in the organization (say, accountant). The accountant will pull out the files that are relevant to their accounting work, and pass the files down to the next person (say, a lawyer). The lawyer performs the same exercise for files that are relevant to their legal role, and so on until all the files have a “home.”In this way, information architecture is about clearly defining roles (accounting role, legal role, etc.) and how those roles interact with data, so that there is a place in the pipeline for the data they utilize. This allows information to flow down the pipeline and end up where it belongs. Note how different this system is from the old information governance model, where organizations would try to classify information by what it was in order to determine where it should be stored. In this new paradigm, we try to classify information by how it is used – because the same piece of content can be used in multiple ways (a vendor contract, for example, can be useful to both legal and accountant roles). The trick to structuring this new architecture is to place data where it is the most useful. Going hand-in-hand with the creation of a new information architecture, cloud migrations can (and should) also be an opportunity for a business culture transformation. Employees may have to re-wire themselves to work within this new digital environment and change the way they interact with data. This cultural transformation can be kicked off by gathering all the key players together and having a conversation about how each currently interacts with data. I often recommend conducting a multi-day workshop where every stakeholder shares what data they use, how they use it, and how they store it. For example, an accountant may explain that when he works on a vendor contract, he pulls the financial information from it and saves it under a different title in a specific location. A lawyer then may explain that when she works on the same vendor contract, she reviews and edits the contract language, and saves it under a different title to a different location. This collaborative conversation is necessary because, without it, no one in the organization would be able to see the full picture of how information moves through the organization. But equally important, what emerges from this kind of workshop is the seeds of culture transformation: a greater awareness from every individual about the role they play in the overall flow of information throughout the company and the importance of their role in the information governance of the organization. Best Practices for Organizations: Involve someone from every relevant role in the organization in the transformation process (i.e. everyone who interacts with data). If you involve frontline workers, the entire organization can embrace the idea that the cloud migration process will be a complete business culture transformation.Once all key players are involved, begin the conversation about how each role interacts with data. This step is key not only for the business cultural transformation, but also for the organization to understand the importance of doing the architecture work.These best practices can help organizations leverage their cloud migration process to achieve an efficient and effective information governance program. To discuss this topic further, please feel free to reach out to me at JHolliday@lighthouseglobal.com. information-governancemicrosoft-365, legal-operationscloud; information-governance; cloud-migration; bloglighthouse

Big data can mean big problems in the ediscovery and compliance world – and those problems can be exponentially more complicated when personal data is involved. Sifting through terabytes of data to ensure that all personal information is identified and protected is becoming an increasingly more painstaking and costly process for attorneys today.Fortunately, advances in artificial intelligence (AI) and analytics technology are changing the landscape and enabling more efficient and accurate detection of personal information within data. Recently, I was fortunate enough to gather a panel of experts together to discuss how AI is enabling legal professionals in the ediscovery, information governance, and compliance arenas to identify personal protected information (PII) and personal health information (PHI) more quickly within large datasets. Below is a summary of our discussion, along with some helpful tips for leveraging AI to detect personal information.Current Methods of Personal Data Identification Similar to the slower adoption of AI and analytics to help with the protection of attorney-client privilege information (compared to the broader adoption of machine learning to identify matter relevant documents), the legal profession has also been slow to leverage technology to help identify and protect personal data. Thus, the identification of personal data remains a very manual and reactive process, where legal professionals review documents one-by-one on each new matter or investigation to find personal information that must be protected from disclosure.This process can be especially burdensome for pharmaceutical and healthcare industries, as there is often much more personal information within the data generated by those organizations, while the risk for failing to protect that information may be higher due to healthcare-specific patient privacy regulations like HIPAA.How Advances in AI Technology Can Improve Personal Data Identification There are a few ways in which AI has advanced over the last few years that make new technology much more effective at identifying personal data:Analyzing More Than Text: AI technology is now capable of analyzing more than just the simple text of a document. It can now also analyze patterns in metadata and other properties of documents, like participants, participant accounts, and domain names. This results in technology that is much more accurate and efficient at identifying data more likely to contain personal information.Leveraging Past Work Product: Newer technology can now also pull in and analyze the coding applied on previous reviews without disrupting workflows in the current matter. This can add incredible efficiency, as documents previously flagged or redacted for personal information can be quickly removed from personal information identification workflows, thus reducing the need for human review. The technology can also help further reduce the amount of attorney review needed at the outset of each matter, as it can use many examples of past work product to train the algorithms (rather than training a model from scratch based on review work in the current matter).Taking Context into Account: Newer technology can now also perform a more complicated analysis of text through algorithms that can better assess the context of a document. For example, advances in Natural Language Processing (NLP) and machine learning can now identify the context in which personal data is often communicated, which helps eliminate previously common false hits like mistakenly flagging phone numbers as social security numbers, etc.Benefits of Leveraging AI and Analytics when Detecting Sensitive DataArguably the biggest benefit to leveraging new AI and analytics technology to detect personal information is cost savings. The manual process of personal information identification is not only slower, but it can also be incredibly expensive. AI can significantly reduce the number of documents legal professionals would need to look through, sometimes by millions of documents. This can translate into millions of dollars in review savings because this work is often performed by legal professionals who are billed at an hourly rate.Not only can AI utilization save money on a specific matter, but it can also be used to analyze an entire legal portfolio so that legal professionals have an accurate sense of where (and how much) personal information resides within an organization’s data. This knowledge can be invaluable when crafting burden arguments for upcoming matters, as well as to better understand the potential costs for new matters (and thus help attorneys make more strategic case decisions).Another key benefit of leveraging AI technology is the accuracy with which this technology can now pinpoint personal data. Not only is human review much less efficient, but it can also lead to mistakes and missed information. This increases the risk for healthcare and pharmaceutical organizations especially, who may face severe penalties for inadvertently producing PHI or PII (particularly if that information ends up in the hands of malevolent actors). Conducting quality control (QC) with the assistance of AI can greatly increase the accuracy of human review and ensure that organizations are not inadvertently producing individuals’ personal information. Best Practices for Utilizing AI and Analytics to Identify Personal DataPrepare in Advance: AI technology should not be an afterthought. Before you are faced with a massive document production on a tight deadline, make sure you understand how AI and analytics tools work and how they can be leveraged for personal data identification. Have technology providers perform proof of concept (POC) analyses with the tools on your data and demonstrate exactly how the tools work. Performing POCs on your data is critical, as every provider’s technology demos well on generic data sets. Once you have settled on the tools you want to use within your organization, ensure your team is trained well and is ready to hit the ground running. This will also help ensure that the technology you choose fits with your internal systems and platforms.Take a Global Team Approach: Prior to leveraging AI and analytics, spend some time working with the right people to define what PII and PHI you have an obligation to identify, redact, or anonymize. Not all personal information will need to be located or redacted on every matter or in every jurisdiction, but defining that scope early will help you leverage the technology for the best use cases.Practice Information Governance: Make sure your organization is maintaining proper control of networks, keeping asset lists up to date, and tracking who the business and technical leads are for each type of asset. Also, make sure that document retention policies are enforced and that your organization is maintaining controls around unstructured data. In short, becoming a captain of your content and running a tight ship will make the entire process of identifying personal information much more efficient.Think Outside the Box: AI and analytics tools are incredibly versatile and can be useful in a myriad of different scenarios that require protecting personal information from disclosure. From data breach remediation to compliance matters, there is no shortage of circumstances that could benefit from the efficiency and accuracy that AI can provide. When analyzing a new AI tool, bring security, IT, and legal groups to the table so they can see the benefits and possibilities for their own teams. Also, investigate your legal spend and have other teams do the same. This will give you a sense of how much money you are currently spending on identifying personal information and what areas can benefit from AI efficiency the most.If you’re interested in learning more about how to leverage AI and analytic technology within your organization or law firm, please see my previous articles on how to build a business case for AI and win over AI naysayers within your organization.To discuss this topic more or to learn how we can help you make an apples-to-apples comparison, feel free to reach out to me at RHellewell@lighthouseglobal.com.data-privacy; ai-and-analyticsai-and-analytics, microsoft-365analytics; data-privacy; ai-big-data; bloglighthouse

One of the unanticipated consequences of the COVID-19 pandemic and the ensuing shift of office employees being forced to work from home, is the impact on counsel who must continue to direct forensically defensible collections for eDiscovery, investigations, and regulatory response scenarios. As employees adjust to remote work, they are increasingly commingling personal data sources, home networks, and corporate data, which in turn creates a wealth of new data sources that will need to be collected as potentially-relevant ESI.In my recent webinar, I discussed this significant shift to the “new normal” of digital digital-forensics and how information governance policies and IT security practices should be proactively extended to remote employees, as well as ways to mitigate future complications around forensic collections that will now need to be almost exclusively remote. Here are a few of the most important aspects to consider on how working from home impacts digital digital-forensics, and practical workflow strategies for handling remote ESI collections.Working from Home: The Digital digital-forensics ImpactThere’s a behavioral impact that automatically comes with working entirely from home, with less delineation between the workday and home life, and subsequently more temptation to use your work laptop for personal reasons. This behavioral impact is also mirrored in the reverse scenario where personal devices become more convenient to use for work. Although we were already seeing quite a bit of intermingling of data pre-COVID-19, this habit is dramatically increasing as home has quickly become the only workplace and there hasn’t been time for organizations to adopt new IT policies to tackle these issues.With the advent of this new remote workplace era, data (mis)management will remain with us for future matters and there will be a permanent impact on collections going forward. Among the top adjustments that need to be made is custodian questionnaires must be enhanced to scrutinize whether any relevant work-related data or communications reside on the custodians’ home devices. The same scrutiny will need to be applied to personal data potentially residing on work laptops as the opportunities for this type of data intermingling or “contamination” will undoubtedly continue to increase.ESI Collections: Practical Workflow StrategiesEven though we’re currently not able to travel onsite to acquire device and data source evidence, we can continue collections by relying on sound and defensible forensic remote strategies that are already in place. Collections from the Cloud are status quo and conducted remotely by definition, but for other ESI sources, we will favor targeted and logical collections over full physical forensic images.For remote collections on premise at an office that’s closed, if there’s a skeleton IT crew in place, screen sharing can be utilized to mimic the exact scenario of a digital-forensics professional being onsite to help load a hard drive or provide access into a server. For custodians sitting at home, the same process can apply and technical guidance can be provided remotely. If shipping is a safety concern, data can be uploaded by secure encrypted file transfer protocol (FTP) using software that can resume broken uploads or by utilizing fast data transfer solutions such as Aspera. Whether figuring out a safe way to transport encrypted hard drives back and forth or using remote data transfer technology, we’ll need to plan for increased turnaround times due to varying upload speeds from home and/or decontamination procedures that are implemented for shipping protocols.Key TakeawaysAs company and personal custodian data commingling grows during COVID-19, a permanent shift is happening in digital digital-forensics and eDiscovery. From a legal standpoint, it’s settled that company-related communication on personal devices is subject to discovery, thus custodian interviews and other information-gathering techniques to identify the relevant scope of a collections effort must be enhanced. And although data preservation and evidence acquisition tasks may take longer to conduct when onsite collections is not an option, the technology is already in place to ensure forensically sound and defensible remote collections now and in the future.To discuss this topic further, please feel free to reach out to me at JBui@lighthouseglobal.com.digital-forensics; information-governancelegal-operations, digital-forensics, information-governancecloud; collections; cloud-security; bloglighthouse

Key ResultsEarly fact-finding with IQ AnswersAvoided weeks of traditional attorney work, resulting in massive savings and a crucial head start.AI-Powered quality control with IQ IssuesAchieved 90% alignment with IQ Issues and counsel’s coding on initial document sample, eliminating the need for a second sampling round.Custom AI Summarizations for second-level reviewDesigned and deployed a custom solution in one week to accelerate second-level review with comprehensive document summaries and key language excerpts.The Challenge: Anticipating Regulatory Scrutiny and Conquering Complex DataA large, multinational consumer platform received a regulatory information request from the Federal Trade Commission (FTC) consumer-protection concerns involving a core offering. Facing a high-stakes investigation, the company’s outside counsel needed to move quickly to interrogate a large volume of data, be proactive with anticipated follow-up requests, and ensure defensibility from the outset.The matter presented two time-sensitive challenges:Fact-Finding: To meet critical regulatory milestones and get ahead of the FTC, counsel needed to immediately interrogate the data and establish key facts. This urgency was compounded by the desire to identify and vet potential custodians in anticipation of the FTC expanding the scope of the information request. Tailored Solutions: A substantial portion of the relevant data was sourced from proprietary exports from two non-standard enterprise collaboration tools. These non-standard CSV-based exports were incompatible with industry-standard eDiscovery chat processing tools, creating a major roadblock for efficient and defensible review.In total, Lighthouse received roughly 4TB of raw data, which translated to just under 1.4M documents requiring processing.The Solution: A Two-Pronged Approach with AI and Custom EngineeringLighthouse partnered with outside counsel to deploy a strategy that leveraged advanced AI for early fact-finding and custom data engineering to normalize the most challenging data sources.IQ Answers: Accelerating Early Case Assessment and Fact-FindingTo address the volatile custodian list and scope, counsel utilized IQ Answers. This allowed the case team to move beyond the traditional “hunt and peck” method of keyword searching and instead use natural language prompts to interrogate the data early and efficiently.With guidance from the Expert Search team, counsel was able to get at their specific goals for early assessment including:Further targeting key data types crucial to the response.Identify potential custodians who were not yet formally in scope.This proactive, AI-driven exploration eliminated the need for weeks of manual attorney review typically required for initial fact-finding. Based on feedback from counsel, Lighthouse determined that one IQ Answers query and response saved 15-20 hours of review time. Thus, the use of IQ Answers allowed outside counsel to realize a savings of more than $400K in the initial case assessment phase.IQ Issues + Summarization: QC and Second-Level EfficiencyOnce the initial culling processes reduced the document population to 47K documents for review, IQ Issues was used as a Quality Control (QC) measure against first-level reviewers.QC Validation: Lighthouse provided a 200-document sample for counsel’s issue coding and generated AI issue codes for the same set. The results showed a remarkable 90% alignment for Responsive documents, significantly exceeding the typical target of 80% alignment. Because of this high precision, the typical second round of sampling was deemed unnecessary.Custom Enhancement: The case team requested a more robust output for second-level review—a comprehensive document summary and key language excerpts to support the issue codes. Leveraging close client collaboration and responsiveness, the Lighthouse team designed a custom solution on an accelerated timeline that added:A full Document SummaryKey Language Excerpts tied directly to the relevant issues.This customized approach was applied to 38K+ documents in the review corpus, and improved the efficiency of the second-level review by an estimated 38%, resulting in an estimated savings of $300K. On top of this, since alignment between the case team and IQ Issues was so high, it’s reasonable to believe these issue codes could eliminate the need for first-level review entirely, which could result in an additional savings of $45K, if not more depending on review volume.Custom Data Engineering for Modern Collaboration DataReceiving proprietary collaboration data in non-standard CSV exports rendered these data types incompatible with typical processing tools. To overcome this, the Lighthouse Innovation Data Engineering team developed a custom, defensible chat processing workflow to handle proprietary CSV exports.The engineered workflow consolidated, cleaned, and structured the data into 24-hour conversational transcripts while embedding attachments and preserving all necessary metadata. This process allowed the complex, non-standard chat data to be transformed into defensible, review-ready datasets, overcoming a significant roadblock that would have stalled the matter for weeks. As an added benefit, this new workflow is now a reusable framework for future custom conversational exports.The Result: Proactive Defensibility and Accelerated ReviewBy combining an array of LighthouseIQ modules with custom data engineering, the corporate team was delivered proactive, high-confidence results:The strategic use of IQ Answers for early fact-finding allowed counsel to anticipate regulatory needs and prepare for potential scope expansion, resulting in $400K+ in savings and a crucial head start.The deployment of IQ Issues provided a high degree of confidence and quality control over the first-level reviewers, evidenced by the 90% alignment score. Moving forward, based on this high alignment, first-level review could be completely eliminated, resulting in $45K in savings.The quickly developed custom summarization solution reduced second-level review time by over a third, resulting in an estimated savings of $300K.The innovative custom chat processing workflow successfully unlocked previously incompatible data sources, turning a complex data roadblock into a defensible, reusable, and review-ready asset.The partnership ensured both the in-house team and their law firm maintained control over the data investigation, allowing them to approach the FTC with confidence and a clear, defensible response.

The Challenge An attorney reviewing an important Gmail message notices that even though the message was sent two years ago, one of the linked attachments was modified just a couple of months ago. How long after the message was sent did the referenced file change? What exactly was altered, and does it have any bearing on her client’s case? Does the version in hand still have meaningful probative value? The e-discovery industry has grappled with “modern attachments”—items that present only as a reference or link to content stored outside the email system—for quite some time. Some are generally static: photographs, training videos, AI-generated images, PDF files, ZIP containers. Others are purpose-built for collaboration, with multiple users contributing content over time, leaving comments, and resolving tasks. Collection, review, and production of this dynamic information can be a moving target. Our Study We set out to gather empirical evidence to evaluate the true scope of the situation. Although this topic has many layers—each with important and often subjective legal implications—we focused narrowly on two questions: What is the ratio of non-editable modern attachments to those that are inherently dynamic and collaborative? In other words, how many of these linked items actually have the ability to evolve in place over time? Of the editable modern attachments, what is the frequency and prevalence of modification after a communication containing the link has been sent? Helpful Metadata The following data elements can be acquired or computed using output from Google Vault and a variety of other acquisition toolkits. Gmail Sent Date/Time: the UTC timestamp of when the message was delivered to Google’s cloud servers for routing to recipients. GDrive Modified Date/Time: the UTC timestamp of the most recent modification to the hyperlinked Google Drive item at the time of collection. The collection event may have taken place hours, days, or many years after the original communication. A change in the Modified Date of an editable linked item after the communication was transmitted is the marker of post-transmittal modification for the purposes of this study.GDrive Item Type: to separate static GDrive items from editable ones, we built an inventory of document types considered collaborative and editable for the purposes of the study. A JPEG is an image unlikely to be edited over time; a Google Sheets file may remain static but also welcomes in-place edits throughout its life. Gmail Age at Collection (Days): the number of days elapsed between when the Gmail message was sent and when the collection event took place. To allow sufficient time for hyperlinked items to potentially undergo modification, we excluded messages where this figure was less than 180 days (approximately six months). This threshold is adjustable for future studies. Findings Using 19 data sets spanning industries and organization sizes, we examined 271,145 hyperlinks to GDrive items from within Gmail messages. Of those, 150,321 links (55%) pointed to items considered collaborative and editable in place, while the remaining 45% pointed to typically non-editable binary files such as images and video. Of the editable corpus, 90,574—60%—were modified in some manner after the Gmail message was sent and prior to the collection event. Caveat Emptor The primary limiting factor in this study is the reliance on the Modified Date of hyperlinked GDrive items as the barometer for substantive change to document content. In Google Workspace, this metadata field is more volatile than it is on a Windows-centric file system such as NTFS or FAT. Simply opening a Google Doc and pressing the space bar causes the item to auto-save and update the Modified Date. Other triggering actions include making or editing comments, resolving comments, updating permissions, and renaming the file. We recognize this is an imperfect metric for evaluating substantive content changes. Due to the volatility of the Modified Date, our tally of modified items necessarily overstates the true frequency of post-transmittal content modification. A second compounding factor: Google Vault follows links anywhere in a Gmail thread, including replied-to body text and forwarded content buried months or years deep. A link from an old message in a long thread gets re-collected at acquisition time, giving it more calendar time to accumulate Modified Date changes without any actual content alteration. This also causes our post-transmittal modification tally to be overstated. Additional Parameters and Implications At the time of publication, Google Vault does not include hyperlinked GDrive items from other potential sources within the Google ecosystem, such as Calendar, GChat, or other Google Docs files. The source data for this initial study therefore focused only on Gmail. It remains unclear whether the type of communication providing the links has a material effect on the resulting metrics. Improvements for Future Studies Google Drive Audit Log—sample events for a Google DocFuture studies should seek to move beyond reliance on GDrive Modified Dates as indicators of substantive content modification. Hash values offer little additional help: the server-side hash values provided by Vault include the Modified Date in the hash computation, further reducing visibility. The following data points and workflows may prove useful: Number of post-transmittal revisions. Fifty subsequent revisions over three months may suggest substantive change; five revisions very soon after transmission may indicate noise such as comment resolution. Number of different contributors. If ten different accounts made post-transmittal changes, this may indicate meaningful collaborative activity, as opposed to a single actor refining a draft. File size. Generally unreliable except for drastic changes—for example, an item that grew from 24KB to 240KB—and even then the change could be entirely attributable to commenting activity. Acquisition of all versions of each hyperlinked item. This would yield a rich dataset but is impractical for most routine e-discovery collections due to data volume, time, and cost. AI may be able to assist with evaluating content changes across versions if provided a thoughtful prompt. Expansion of source communication types. This study focused on Gmail, but many other sources warrant consideration: Google Calendar and Groups, GChat and in-meeting messaging, Slack channels and direct messages, text messages, WhatsApp, Telegram, Discord, Signal, and others. Google Drive Audit Log. The audit log may provide the clearest window into whether a document’s content—as distinct from its metadata, comments, or permissions—was actually changed. It records events with enough granularity to distinguish views, comments, renames, permission changes, and true edits. Filtering to “edit” events alone would be a meaningful improvement over relying on Modified Date. Key challenges include: Default log retention of only six months, though events can be ported continuously to SIEM systems such as Google Security Operations or Splunk. Potentially tens of thousands of entries per GDrive item, requiring targeted filtering at collection time. High-level tenant access requirements, particularly for pulling data via the Reports API and/or Drive Activity API. Unavailability of equivalent log data for personal Google accounts not part of a Workspace tenant. Final Thoughts The data points leveraged in this initial study are admittedly imperfect, but more precise metrics are coming within reach as our industry’s tools and collective knowledge about collaboration platforms improve. We hope this inaugural research leads to further discoveries that better inform discovery practitioners and the courts. Tools & Further Reading Lighthouse’s Linked Files Solution for Google Workspace Lighthouse’s Modern Data SolutionsCraig Ball, “A Dog and Its Tail: Don’t Let Version Uncertainty Cloud Linked Attachment Production”Metaspike’s Forensic Email Collector‍

ChallengeWhen a whistleblower allegation placed a company’s future in question, outside counsel needed answers immediately. Executives had to determine whether the allegations were credible, whether fraud or criminal exposure existed, and whether the business could continue operating—all within days.The investigation involved more than 126,000 documents spanning email, collaboration platforms, messaging applications, and business records. A traditional review would have required weeks before attorneys could begin developing meaningful legal strategy.With critical business decisions on the line, an Am Law 50 firm partnered with Lighthouse to rapidly identify the evidence that mattered most while maintaining a defensible investigative process.SolutionUsing IQ Case Strategy, Lighthouse and outside counsel replaced the traditional review-first approach with a question-first investigative methodology.Rather than reviewing every document equally, counsel first identified the legal and factual questions that would determine the outcome of the investigation. Lighthouse then transformed those questions into a structured investigative workflow that rapidly surfaced the evidence most relevant to each issue.Approximately 150 targeted investigative questions guided the analysis across more than 126,000 documents, including email, WhatsApp, Signal, Discord, ChatGPT conversations, and other business records.As the investigation progressed, Lighthouse continuously identified, organized, and synthesized the most relevant evidence while attorneys refined investigative priorities and validated findings. Instead of spending weeks reviewing low-value material, counsel could immediately focus on the documents that informed legal strategy and business decisions.IQ Case Strategy delivered a comprehensive merits assessment that included:Executive summary of key findings, risks, and unresolved questionsPrioritized hot documents supporting and challenging each allegationIssue-based evidence summaries with integrated chronologiesStructured evaluation of competing factual theoriesDocument-level investigative coding and supporting data indexThe result was more than a streamlined document review—it was an evidence-based strategic assessment that enabled counsel to quickly evaluate risk, advise leadership, and determine the appropriate path forward.ImpactThe investigation produced meaningful business results in less than three days.Completed the investigation in under 72 hoursReduced 126,000 documents to approximately 1,100 critical recordsGenerated more than $335,000 in estimated cost savings compared to traditional review methodsEnabled counsel to rapidly assess the credibility of the allegations, potential fraud, criminal exposure, and operational riskEquipped executive leadership with evidence-based guidance to make critical business decisions within days rather than weeksThe Lighthouse DifferenceHigh-stakes investigations demand more than faster document review—they demand earlier insight, stronger strategy, and defensible outcomes.IQ Case Strategy combines AI-powered analysis with the expertise of legal strategists, investigators, and technologists to transform complex legal questions into actionable intelligence. By identifying the evidence that matters most at the outset of an investigation, legal teams can assess risk sooner, test legal theories more effectively, and provide business leaders with the confidence to make informed decisions.Whether responding to whistleblower allegations, regulatory inquiries, internal investigations, or complex disputes, Lighthouse helps organizations reduce review time and cost while accelerating the path from information to insight.When every decision matters, IQ Case Strategy empowers legal teams to move beyond document review and deliver what clients need most: actionable intelligence that drives confident, strategic decisions.

The Challenge A national healthcare provider faced 14 related matters across 9 jurisdictions, with 11M documents dispersed across multiple vendors, databases, and case teams.Redundant Review Is a Data Problem, Not a Legal OneWith a traditional eDiscovery model, each matter would have required reprocessing, rehosting, and/or rereviewing large portions of the same data. Data insights and work product would be siloed inside individual matters and within disparate legal teams. This would severely escalate costs and drive inconsistent outcomes and operational drag.The SolutionLighthouse recognized that the problem wasn’t just data volume. It was the absence of a system that could learn across matters and apply that intelligence forward. With LighthouseIQ, counsel could take a fundamentally different approach—using a centralized, AI-backed data system guided by expert judgment, where decisions, insights, and work product flow seamlessly between matters and legal teams.AI-Backed ResultsReduced 11M documents to 90K requiring reviewReused 100K coding decisions across 14 related mattersAvoided duplicate hosting, processing, and review of 1.2M documentsEnabled instant productions from a national database with LighthouseIQ$650K in cost savings delivered with consistency and defensibility built in, not traded offBuilding Human-Guided AI at Multidistrict ScaleStep 1: An AI-Powered Data Repository, Expertly DesignedLighthouse migrated all 11M documents (from both Relativity and non-Relativity sources) into a single LighthouseIQ hosting environment. Lighthouse experts designed the repository architecture upfront to support cross-matter reuse and long-term litigation strategy.Lighthouse eliminated duplicate hosting, processing, and review of 1.2M documents.Step 2: AI Normalization and Cross-Matter MatchingWithin the repository, LighthouseIQ normalized documents and applied proprietary hashing to identify duplicates, near-duplicates, and previously reviewed content across matters. Lighthouse experts validated how matches and inherited decisions were applied, ensuring accuracy, consistency, and defensibility across jurisdictions.Lighthouse reused 100K coding decisions across matters.Step 3: AI-Guided Prioritization, Expert Review StrategyLighthouse review experts designed one strategic review plan for all 14 matters that lowered costs and maximized data reuse and cross-matter insights. Using cross-matter intelligence, IQ Review identified 150K documents (from within the 11M housed in the repository) that were most likely to be responsive across jurisdictions.This dataset was published to the national review database and fully reviewed by an experienced Lighthouse review team (trained by Lighthouse review managers) to categorize each document for both national and jurisdictional responsiveness. After review, Lighthouse copied this strategic production set to each jurisdictional database. This approach kept hosting costs drastically lower for each individual matter, while providing all local case teams with an immediate first production, well ahead of production deadlines.Out of 11M documents, just 90K required human review.Step 4: Continuous Learning Through a Human-in-the-Loop Feedback CycleAfter production, expert-approved coding decisions were fed back into the repository. LighthouseIQ automatically matched those decisions to corresponding documents across matters, creating immediate efficiencies while preserving expert intent. With every matter, the system became: more informed, more consistent, more cost-effective.‍The Results: A System That Gets Smarter Over TimeBy using LighthouseIQ, a sprawling, multidistrict litigation environment was transformed into a reusable intelligence system. The client achieved significant cost savings and faster productions, without sacrificing judgment, consistency, or defensibility.In the process, LighthouseIQ delivered $650K in cost savings.

The ClientThe client operates at the forefront of AI innovation while simultaneously navigating heightened regulatory oversight and increasingly complex civil litigation.The Legal ChallengeOver the past two years, this client has faced a sharp increase in high-profile, high-stakes litigation and regulatory investigations. Matters often involve novel technologies, modern collaboration and messaging platforms, and compressed response timelines.This was creating sustained pressure on traditional eDiscovery models and legacy discovery tools, which proved to be inefficient and difficult to scale at the speed required. Repeated data recollection, redundant review, and inconsistent issue identification also introduced unnecessary costs and risks. The client needed an approach that could apply intelligence across matters, learn from prior work, and deliver defensible results quickly.The Lighthouse SolutionWe implemented a LighthouseIQ-driven eDiscovery program capable of scaling across the client’s litigation and investigative portfolio, prioritizing the client’s need for speed, consistency, and defensibility. Through the rapid design and deployment of this framework, Lighthouse has helped the client:Meet aggressive discovery and regulatory obligationsReduce eDiscovery risk across multiple concurrent mattersSave hundreds of thousands of dollars in just a few monthsMaintain consistency, defensibility, and institutional knowledge across a growing litigation portfolioAs new matters arise, the program continues to scale, leveraging prior AI-driven insights rather than restarting the discovery process with each engagement.Pillars of the LighthouseIQ eDiscovery ProgramIn 2024, Lighthouse launched a programmatic eDiscovery initiative for this client that was grounded in what would become the LighthouseIQ platform and application suite. The objective was to move beyond point solutions and instead create an adaptive framework that continuously improves as new matters arise. The pillars of this framework and the results achieved in just the first year are below.Reusing Work Product at Scale with LLM-Backed TechnologyLighthouse built a centralized data repository designed specifically to support work product reuse across litigation. Each matter is maintained in its own siloed workspace, where LighthouseIQ is used to:Identify when prior work product is relevant to new mattersReuse review decisions, key documents, and privilege determinationsControl reuse across matters while maintaining strict, matter-level silos for privilege and confidentialityThe result:Reduced unnecessary recollection and reprocessing across litigation by over 10 terabytesSaved tens of thousands of dollars by minimizing duplicative attorney review while improving cross-matter consistencyAccelerating Fact Development Under Regulatory DeadlinesThe impact of LighthouseIQ has also been pronounced in matters requiring rapid issue and document identification under regulatory pressure. In a recent regulatory inquiry, outside counsel had only days to identify critical facts from hundreds of thousands of documents. This timeline would have been impossible to achieve using traditional search and review technology.Lighthouse deployed IQ Case Strategy to:Rapidly analyze hundreds of thousands of documentsSurface the key documents tied to three core legal issuesPrioritize results for attorney review within daysThe result:Reduced review costs by more than $100,000Completed the regulatory response within two weeksDelivered the documents attorneys needed within days (vs. the months it would have taken with traditional search tools), giving them more time to work on data-backed legal analysisBuilding a Defensible Forensics FoundationLighthouse also designed and implemented a centralized forensics collection program spanning all of the client’s major data sources, including:Google Vault and Google DriveSlackMobile devicesNon-standard messaging and social applicationsThe forensic program addressed nuanced challenges that arise in modern data environments, including the preservation, collection, and treatment of hyperlinked attachments, particularly where contemporaneous versions are unavailable. Lighthouse’s forensic team improved collection efficiency and defensibility by:Developing a core forensics playbook to standardize data retrieval across mattersDesigning targeted collection workflows that leverage usage and access patterns to prioritize files actually accessed by custodians, significantly reducing over-collectionThe result:Improved collection consistency across matters while minimizing unnecessary data processing and review via a repeatable forensic program

Background Regulators issued a sweeping investigation tied to a global company’s high-profile acquisition. The scope and timeline were demanding: more than 30TB of data required analysis, risk assessment, and production in less than 30 days. A defensible, scalable approach that met regulatory requirements while controlling cost, mitigating risk, and ensuring flawless execution was non-negotiable. The Lighthouse Approach LighthouseIQ eliminated unnecessary human review, decreased scope early, surfaced risk faster, and executed at scale without errors. Key elements included: IQ Review used AI to surface only what truly required human judgement. In parallel, a 300-person managed review team was rapidly ramped to handle the doc volume. IQ Priv accelerated privilege identification and used generative AI for privilege log drafting and names list creation. Key documents identified via modeling in parallel to review. Cross-matter analytics and work-product reuse across a parallel antitrust litigation Custom operational workflows, including M365 cloud attachment linking and secure reuse repositories All of it executed in parallel. No bottlenecks. No rework. Results 10TB, including 20M images were produced with M365 cloud attachments as required by regulators in under 60 days. This delivery boasted an 100% error-free production result and $20M in total cost savings. Cost avoided:

When an engineering partner suddenly pulled out of a major project, a global manufacturer needed answers fast. Was the termination allowed under the contract, or had the partner crossed a line that could lead to litigation? The company’s law firm had to move quickly. A deadline was approaching to file a termination claim, but that was only the first step. Once the partner responded, the firm expected tough follow-up discovery. To be ready, they needed to understand the full story before the dispute escalated. The firm identified and collected more than one million documents across fifteen custodians, most in the United States. While this is a large but not uncommon volume of data for such a complex investigation, the real challenge was determining how to interrogate it quickly without iterating dozens of times on keywords as is the case with traditional keyword search. As one attorney explained, “Most of the time, we don’t know the exact words people used and everyone uses different language anyway.” Every guess costs time, and every missed variation risks overlooking critical evidence. The Lighthouse ApproachLooking for a faster and more reliable approach, the firm used IQ Answers directly inside their Relativity environment, starting with Microsoft 365 data from the U.S. custodians. IQ Answers is not a general-purpose chatbot. It’s an enterprise AI tool that leverages large language models and other AI and ML models to answer questions but is grounded solely in the documents in your case. Instead of building complex keyword searches, attorneys simply asked questions and received clear, document-backed answers. Using this approach, the team conducted an early case assessment without relying on months of manual review. Once all documents were loaded, they used the AI to explore the data directly. Over the course of less than two months, the team asked 182 natural-language questions. That process captured 6,325 documents, of which the team flagged 835 as potentially important. To confirm the results, the firm conducted a second-level manual review of those documents. Attorneys validated 190 documents as key evidence and identified another 130 as potentially key. Notably, 832 of the 835 documents directly related to the 14 issues identified for the case. By combining AI-driven discovery with focused human review, the team turned an overwhelming volume of data into clear, actionable insight—delivering results in a fraction of the time required by traditional methods. Based on the intelligence IQ Answers delivered, the firm made a critical strategic decision: they opted against a full review. What would have been months of traditional document review and significant expense became a targeted, AI-driven investigation that gave them exactly what they needed in pre-litigation.

BackgroundThe client faced a high-stakes Hart Scott Rodino (HSR) Second Request with tight compliance deadlines under FTC oversight:7.5M documents (7.8TB) were collected in 2 phases from 28 custodians collected across email, collaboration platforms, mobile data, and hard copy sources.Differentiated responsiveness standards between groups of custodians, requiring tailored review strategies.The matter was re-opened months later and additional documents requested.The Lighthouse ApproachThe team implemented both IQ Review and IQ Priv, combining AI analysis with disciplined managed review execution. Key elements included: AI-supported relevance review and a team of 30 contract attorneys for documents that required eyes-on review Privilege review, privilege log and names legend automation via AIAI image analysis for visual and scanned contentTwo separate AI models were trained to address differing responsiveness criteria across custodial groups, ensuring precision without sacrificing defensibility.ResultsLighthouse successfully processed 7.5M total documents across both collections. Because our AI models remain largely stable even with new documents, analysis of the second phase of collection was able to start immediately. LighthouseIQ powered analysis meant that only 2,500 contract review hours were needed in total. Using AI insights, our contract review attorneys maintained high review velocity across responsiveness, privilege, and PII review streams. The approach delivered FTC-ready defensibility under close regulatory scrutiny while enabling rapid adaptation to an evolving regulatory scope. Through expert coordination across legal, technical, and review teams, the engagement delivered predictable, consistent performance even under compressed timelines and shifting requirements.

Key Events and OutcomesClient initiated an internal investigation into executive misconduct, requiring high-precision document discovery and behavioral analysis.Multiple search workstreams delivered thematic overviews, interview prep kits, and targeted behavioral evidence.Lighthouse search experts across multiple time zones gave the case team access to support whenever priorities shifted.Lighthouse delivered 160 total key documents over four days in three deliveries to accelerate the time to knowledge and minimize the risk of missing critical evidence.Counsel prepared more strategic witness interviews using behavioral evidence and operational insights surfaced by IQ Answers and Lighthouse search experts.What Was NeededA large retailer launched an internal investigation after receiving whistleblower allegations of misconduct. The project required rapid, high-precision document discovery and behavioral analysis across a substantial volume of internal communications. The client needed thematic overviews of key communications, curated document sets to support interview preparation, and targeted behavioral insights to inform legal and internal review. All work had to be completed within a single week to enable critical witness interviews and support preparation of a summary report for outside counsel.ProcessThe first step in triaging the needs related to the matter involved outside counsel conducting initial research using IQ Answers. This early facts assessment confirmed that the central concerns of the investigation were reflected in the data, and helped refine the goals and targets for a hand-off to the Lighthouse expert search team.Leveraging insights from counsel’s initial use of IQ Answers, the expert search team used advanced techniques developed within Lighthouse’s proprietary systems to support Key Document Identification workflows. These methods, combined with tagging and document filtering workflows for compliance and legal review, targeted queries of linguistic patterns, indicators of tone and behavior, and other expressions of language.In parallel, IQ Answers powered conceptual and semantic queries, surfacing nuanced patterns and sentiment indicators across a voluminous set of 300,000 documents. The combined technology and workflow approach allowed Lighthouse's expert search team to precisely identify the documents of highest importance and potential impact for the investigative team. Volume-reduction methodologies were applied to isolate the most likely relevant, non-duplicative content. Linguistic and behavioral searches focused on topics prioritized by counsel, with results delivered on a rolling basis to support interview preparation and legal review.Throughout the project, Lighthouse's expert search team worked in close coordination with the matter team, leaning on global coverage to provide seamless support and incorporating feedback into iterative search cycles.Expert Search ResultsLighthouse's expert search team delivered three waves of curated document sets totaling approximately 160 records, each tagged with topics and annotation fields to support rapid review. This enabled highly targeted interview preparation by surfacing behavioral indicators, communication patterns, and operational insights relevant to the investigation. The outputs integrated into client workflows, including saved searches and coding layouts within the review platform.By combining multiple information retrieval, analysis, and synthesis technologies and augmenting with human expertise, the team surfaced unique documents responsive to similar lines of inquiry—providing broader and more comprehensive information coverage in a shorter time frame than any single approach could have achieved alone.

A global consumer products company with a distributed workforce needed to strengthen its information security posture. With sensitive intellectual property, regulatory obligations across multiple jurisdictions, and increasing use of Microsoft 365 collaboration tools, the security team sought a more resilient approach to protecting critical data against leakage, misuse, or unauthorized access. Challenge The existing environment lacked unified policies for sensitivity labeling, retention, and data loss prevention, making it difficult to enforce consistent governance across all business units. The client faced significant risks around: Data leakage from collaboration data in Microsoft Teams, SharePoint, and OneDrive. Lack of consistent data classification leading to overexposed sensitive content. Insufficient DLP controls for email and cloud-based sharing, creating regulatory and reputational risks. Growing compliance pressure across global operations, requiring alignment with GDPR, CCPA, and industry-specific regulations. Solution Lighthouse partnered with the client to design a comprehensive Microsoft Purview Information Protection and Data Loss Prevention (DLP) framework pilot that could scale globally. The solution included: This design provided the foundation for both proactive risk reduction and reactive incident handling. Results Through this engagement, the client achieved: Reduced risk of data exposure by applying consistent labeling and DLP rules across collaboration platforms. Improved regulatory compliance by aligning information protection policies with global privacy and industry frameworks. Enhanced incident visibility with reporting dashboards and adaptive policies that alerted security teams to high-risk events. Sustainable governance model enabling scalability as new collaboration tools and AI-driven workflows are adopted. Why It Matters As global enterprises accelerate digital collaboration, data security gaps in Microsoft 365 environments can create regulatory, financial, and reputational risk. By implementing a comprehensive governance and DLP framework, organizations can protect their most valuable assets: intellectual property, customer data, and regulated records, while enabling employees to work securely across borders. This project highlights how a well-designed information protection program, supported by Microsoft Purview, can simultaneously strengthen security and simplify compliance for multinational companies.

The ChallengeA major media company received a Letter of Inquiry (LOI) from the FCC, triggering a high-stakes regulatory investigation. The company was required to produce relevant communications within a month—but two weeks in, the legal team still needed to collect over 2 million documents from 16 custodians. Complicating matters further, the company’s software platform was mid-transition, raising serious concerns about data integrity and reporting reliability. The SolutionRecognizing the urgency and complexity of the matter, the media company and its outside counsel turned to Lighthouse. With an immense volume of documents, looming regulatory deadlines, and a technology transition in progress, they needed more than linear review—they needed a strategic partner with forensic, eDiscovery project management, and AI expertise. Within just four days, Lighthouse’s forensics experts collaborated with the company to collect and process all relevant custodian data, including associated family files. From there, our project management team worked with the company and its counsel to apply targeted filtering—focusing on communications between key senders and recipients. This reduced the original 2 million documents to a refined universe of 94,000. Using advanced email threading and junk file analysis, the team further reduced the review set to 59,000 documents. Given the aggressive timeline, volume of documents, and the dataset’s low privilege risk, Lighthouse consultants recommended deploying Relativity aiR for Review. Working closely with inhouse and outside counsel, Lighthouse developed a defensible AI review prompt using an iterative sampling workflow designed to meet stringent recall standards and maximize precision. Only 300 documents were reviewed during this iterative phase. Relativity aiR identified a predicted responsive universe of 28,000 documents. A first-level review was completed in just five days, followed by a quality control review conducted by outside counsel. Final validation confirmed 88% recall and 96% precision—exceeding regulatory and eDiscovery defensibility standards. The ResultsUltimately, 18,000 documents were successfully produced on time, along with an expert declaration on the defensibility of the process from a Lighthouse Strategic Consultant. When the FCC issued a supplemental request, the teams were able to use the aiR-powered workflow once again to quickly review 2,000 additional documents—resulting in the production of the 300 relevant files.

Client: Global academic medical system Stakeholders: CISO, Information Governance, Legal Tech stack: Microsoft 365 + Microsoft Purview (SharePoint, OneDrive, Exchange, Teams) Objective: Identify, label, and protect high‑value IP across M365 Business Challenge Conventional pattern matching missed nuanced research content; labels were inconsistent. Emerging IP taxonomy lacked consistent, enforceable labels across repositories. Conventional pattern matching couldn’t reliably detect unstructured, nuanced IP. Teams needed clarity on when to use Sensitive Info Types (SITs), Exact Data Match (EDM), and Trainable Classifiers, and how to govern them. Wanted to compare outcomes with prior third‑party classifiers. What Lighthouse Did IP Taxonomy + Purview Labels Mapped proprietary IP categories to a label set Blended Classifier Strategy Combined SIT, EDM, and Trainable Classifiers Operationalize Piloted and tuned models aligned with retention/legal hold/eDiscovery, with auto‑labeling and user prompts. Controls Developed change‑management materials Outcomes Common IP Language: Agreed taxonomy mapped to enforceable labels. High‑Confidence Detection: Trainable classifiers surfaced custom IP Consistent Protection: High‑value content auto‑labeled with policy‑driven controls in M365. Governed Workflows: Clear guidance on SIT vs EDM vs Trainable; fewer false positives/negatives; faster to eDiscovery. Timeline Weeks 0–1 — Kickoff + Plan Weeks 2–4 — Design + Setup Weeks 5–9 — Run Pilots Weeks 10–11 — High Level Design + Training Why Microsoft Purview for Data Protection Enterprise-wide strategy - Unified data security, governance, compliance Integrated governance - DLP, retention, legal hold, eDiscovery Flexible detection models - Sensitive Info Types, Exact Data Match, Trainable Classifiers Persistent, label-based protection - Embedded permissions travel with data

During the first trial of a high-stakes, serialized product liability case, a legal team was stunned when opposing counsel introduced damaging documents that had originated from the client’s own production. Despite months of review by both in-house and outside counsel, these documents had slipped through the cracks—threatening to derail the case and set a dangerous precedent for future litigation.With millions at stake, they needed a solution—and fast. They turned to Lighthouse. In just three days, a Lighthouse expert dug into the 25,000 documents in the production, uncovering 14 crucial rebuttal documents that counsel used at trial to challenge the plaintiffs’ claims.This swift, targeted response set the client up for success and in a position to save the company millions of dollars in judgments and protect them from similar challenges in upcoming cases.In the world of serialized product liability litigation, where one case can impact many others, the power of rapid, focused eDiscovery can be the difference between a costly loss and a decisive win.

SolutionThe Director of Information Security partnered with Lighthouse to conduct a comprehensive scan using Lighthouse’s proprietary environment scan technology and Microsoft Information Protection (MIP). This scan could locate sensitive data across the enterprise and provide the necessary visibility to roll out full MIP policies.1. Lighthouse’s Comprehensive Environment Scan Lighthouse’s scan helped identify and locate sensitive data, helping the security team to understand its exposure and design its protection strategy. An example of findings included:Teams: /LegacyRightAngleData contained 139,000+ instances of sensitive data. SharePoint: /Financial_DMS stored 52,000+ instances of sensitive data. OneDrive: /[single employee] held 18,900+ instances of sensitive data. Most Common Sensitive Data TypesABA Routing NumbersEU Passports NumbersSWIFT CodesU.K. National Health identifiers2. Created Sensitivity Labels in Pilot Mode Following the scan, Lighthouse supported the security team in developing sensitivity labels in pilot phase, including: Testing Auto-Labeling & Classification: Defining initial label rules based on scan results. Evaluating Impact Before Full Rollout: Assessing how sensitivity labels functioned across departments and workflows.Preparing for Future Policy Implementation: Establishing a structured data protection strategy before MIP policies were fully deployed. Key Outcomes The Lighthouse environment scan gave the organization critical visibility into sensitive data locations, laying the groundwork for stronger data governance, protection, and compliance. Critical Visibility for Future Protection: Identified where sensitive data resided to guide security and governance efforts. Pilot Sensitivity Labeling Program: Launched sensitivity labels to test the efficacy of policies and refine data governance practices. Foundation for MIP Rollout: Positioned the team to automate protection and enforce compliance through Microsoft Purview. The Lighthouse environment scan helped the client uncover hidden risks and build a foundation for stronger data governance. With clear visibility and a pilot labelling program, the organization is prepared to advance its Microsoft Purview rollout and reduce exposure.

The ChallengeRecently, the U.S. Department of Justice (DOJ) issued a broad and urgent HSR Second Request in connection with a high-profile merger for a large, highly-regulated corporation. The regulatory inquiry required fast, defensible data collection from a range of custodians, many of whom were senior executives. With just weeks to act, the stakes were clear: respond efficiently and thoroughly or risk delaying the transaction’s approval.The request included nearly 30 custodians spread across the U.S., many with privacy sensitivities around their mobile data.The SolutionLighthouse assembled a cross-functional team of digital forensics experts and client services professionals to lead a high-touch, high-urgency workflow. Coordination between the digital forensics project manager and client services project manager ensured that collections, handoffs, and processing moved forward without bottlenecks—driven by daily alignment and real-time communication.Over six weeks, Lighthouse collected mobile data from all 27 custodians using a mix of remote and on-site methods, all handled in-house to minimize disruption and maintain control. The team leveraged industry-standard tools and proprietary workflows to extract encrypted messaging data from apps like WhatsApp and Signal, even when on-site collection was required. To address privacy concerns, Lighthouse implemented a workflow where custodians approved contact lists before any messages were filtered and prepared for review. This approach ensured rapid turnaround—often within one business day—without compromising data integrity or custodian trust.ResultsBy strategically splitting collections between remote and on-site, the Lighthouse team accelerated the project, completing collection in just 1.5 months and saving an estimated 60 hours of work time. More importantly, the client was able to respond to the DOJ within deadline—and was armed with complete, accurate, and defensible data drawn from even the most sensitive mobile sources.

Challenges The legacy environments included approximately 2,200 repositories with structured data. IT aimed to decommission these systems to reduce costs, while Legal needed confirmation that legal holds were preserved before signing off on each system. The client also had to navigate international data privacy regulations, particularly when data consolidation meant data was moved across borders. Initially, two service providers split the responsibilities: the General Counsel’s office engaged one, and the eDiscovery department turned to Lighthouse because they had a long-term relationship. This fragmented approach introduced inefficiencies and risk.Solutions Lighthouse consultants:Liaised between Legal and IT, validating preservation plans for each repository. Built a detailed playbook, documentation standards, and a quality control process to provide consistency across the project. Conducted regular review calls with IT. Approved or rejected plans based on standards defined by Legal, ensuring retrieval capabilities, immutability, and long-term access. When the client saw our approach, they consolidated the work under Lighthouse, extended the engagement by 24 months, and rescoped the project.Wins The acquiring company: Gained a defensible, repeatable preservation process aligned with legal and regulatory obligations. Decommissioned costly legacy systems while maintaining legal hold compliance. Improved coordination between Legal and IT, expediting approvals. Mitigated regulatory risk by tracking and documenting preservation decisions. Ensured cross-border data preservation aligned with jurisdictional privacy regulations.Reduced long-term operational costs by retiring expensive platforms.Take Aways This project illustrates how cross-departmental cooperation can reduce risk and costs in post-acquisition decommissions and rationalizations. With a well-designed playbook and a team fluent in legal obligations and technical systems, the client adopted a defensible preservation strategy and unlocked long-term savings.

The project included replacing expensive third-party archives with native tools in M365, utilizing an automation solution that Lighthouse had recently prototyped for a large global manufacturer, and other breakthroughs the institution was unable to make before engaging with Lighthouse. Our work with the institution helped unblock their Microsoft 365 deployment and ultimately led to disclosure to regulators for institution’s intent to use M365 as system of record.SIFIs have long wished for a better way to meet their mutability requirement. Historically, they have relied on archiving solutions, which were designed years ago and are poorly suited for the data types and volume we have today. For years, people in the industry have been saying, “Someday we’ll be able to move away from our archives.” It wasn’t until the introduction of M365 native tools for legal and compliance that “someday” became possible.Data Management for SIFIs is Exceptionally ComplexThe financial services industry is one of the most highly regulated and litigious sectors in the world. As a result, companies tend to approach transformation gradually, adopting innovations only after technology has settled and the regulatory and legal landscape has evolved.However, the rate of change in the contemporary world has pushed many financial heavyweights into a corner: They can continue struggling with outdated, clunky, inadequate technologies, or they can embrace change and the disruption and opportunities that come with it.From an eDiscovery perspective, there are three unique challenges: (1) as a broker-dealers, they have a need to retain certain documents in accordance with specific regulatory requirements that govern the duration and manner of storage for certain regulated records, including communications (note that the manner of storage must be “immutable”). This has traditionally required the use of third-party archive solutions that has included basic e-discovery functionality. (2) As a highly regulated company with sizable investigation and litigation matters, they have a need to preserve data in connection with large volumes of matters. Traditionally, preservation was satisfied by long-term retention (coupled by immutable storage) and without deletion. Today, however, companies seek to dispose of legacy data—assuming it is expired and not under legal hold—and are eager to adopt processes and tools to help in this endeavor. (3) They have a need to collect and produce large volumes of data—sometimes in a short timeframe and without the ability to cull-in-place. This means they are challenged by native tooling that might not complete the scale and size of their operations. This particular company’s mission was clear: to use M365 as a native archive and source of data for eDiscovery purposes. To meet this mission, Lighthouse needed to establish that the platform could meet immutability and retrievability requirements—at scale and in the timeframe needed for regulatory and litigation matters. Lighthouse Helps a Large Financial Institution Leverage M365 to Replace Its Legacy Archive SolutionLighthouse is perfectly positioned to partner with financial services and insurance organizations ready to embrace change. Many on our team previously held in-house legal and technology roles at these or related organizations, including former in-house counsel, former regulators, and former heads of eDiscovery and Information Governance. Our team’s unique expertise was a major factor in earning the trust and business of a major global bank (“the Bank”). The Bank first engaged with Lighthouse in 2018, when we conducted an M365 workshop demonstrating what was possible within the platform—most notably, at the time, the potential for native tools to replace their third-party archives. Following the workshop, the Bank attempted, together with Microsoft, to find a viable solution. These efforts stalled, however, due to the complexity of the Bank’s myriad requirements. In 2020, the Bank re-engaged Lighthouse to supports its efforts to fully deploy Exchange and Teams and, in doing so, to utilize the native information governance and e-discovery toolset, paving way for the Bank to abandon its use of third-party archiving tools for M365 data. Our account team had the nuanced understanding of industry regulations, litigation and regulatory landscape, and true technical requirements needed to support a defensible deployment.As a result, we were able to drive three critical outcomes that the bank and Microsoft had not been able to on their own: (1) A solution adequate to meeting regulatory requirements (including immutability and retrievability). (2) A solution adequate to meeting the massive scale required at an institution like this. (3) A realistic implementation timeline and set of requirementsLighthouse Ushers the Bank Through Technical and Industry MilestonesWe spent six months designing and testing an M365-based solution to support recording keeping and e-discovery requirements for Teams and Exchange (including those that could support the massive scalability requirements). The results of these initial tests identified several gaps that Microsoft committed to close. The six month marked a huge milestone for the financial services industry, as the Bank disclosed to regulators their intent to use M365 as system of record. This showed extreme confidence in Lighthouse’s roadmap for the Bank, since a disclosure of this nature is an official notice and cannot be walked back easily. Over the next few months, we continued to design and test, partnering with Microsoft to create a sandbox environment where new M365 features were deployed to the Bank prior to general availability, to ensure we were able to validate adequate performance. During this time, Microsoft made a series of significant updates to extend functionality and close performance gaps to meet the Bank’s requirements. Finally, in February 2021, all the Bank’s requirements had been met and they went live with Teams—the first of their M365 workload deployments. That configuration of M365 met only some of the Bank’s need, however, so Lighthouse had to enable additional orchestration and automation on top. As it happens, we had recently done this for another company, creating a proof of concept for a reusable automation framework designed to scale eDiscovery and compliance operations within M365. Building on this work, we were able to quickly launch development of a custom automation solution for the Bank. This project is currently underway and is slated to complete in June, coinciding with their deployment of Exchange Online.Lighthouse Enables Adoption of Teams and Exchange and Scales M365 Compliance FunctionalityCompliant storage of M365 communications using native tools, rather than a third-party archive. Scaled and efficient use of M365 eDiscovery, including automation to handle preservation and collection tasks rather than manual processes or simple PowerShell scripts.Improved update monitoring, replacing an IT- and message-center-driven process with a cross-functional governance framework based on our CloudCompass M365 update monitoring and impact assessment for legal and compliance teams.Framework for compliant onboarding of new M365 communication sources like Yammer. Framework for compliant implementation of M365 in new jurisdictions, including restricted country solutions for Switzerland and Monaco. Framework to begin expanding to related use cases within M365, such as compliance and insider risk management. Lighthouse Paves the Way for Broader M365 Adoption Across the Financial Services IndustryFollowing the success of this project, we have been engaged by a dozen other large financial institutions interested in pursuing a similar roadmap. The roadblocks we removed for the Bank are shared across the sector, so the project was carefully watched. With the Bank’s goals confidently achieved and even surpassed, its peers are ready to begin their own journey to sunset their archives and embrace the opportunities of native legal and compliance tools in M365.

Firms Needed Fast Analysis of 25M Documents More than a dozen international law firms—including a Joint Defense Group (JDG) of 11 firms and several firms representing defendants outside the JDG—were engaged in a complex cluster of cases spanning over 30 US jurisdictions. The total document tranche included over 25M documents. The firms needed to find and understand the key players, timelines, and nuances involved in each litigation, while also preparing for hundreds of depositions, witness interviews, hearings, and trials scheduled across the litigation universe. However, traditional approaches to fact-finding and litigation (i.e., document review, keyword searches, etc.) were drowning case teams in extraneous and duplicative information. They came to Lighthouse looking for a strategic, unified approach to fact-finding, led by experts who could deliver the key documents, information, and details the case teams needed—and nothing more. Custom Workflows Power Consistency, Speed, and Efficiency Our experts started by creating a topic map across matters, which helped them quickly provide case teams with the core themes in each jurisdiction while reducing redundant search work. From there, as case strategy for each matter developed, the Lighthouse team drilled down into more nuanced fact-finding to help surface the documents case teams needed to learn the key details of each matter, through strategies like: State/Jurisdictional Overview Workflow – We used advanced search technology to target key documents in incoming productions and categorize them by jurisdiction, providing case teams with an immediate thematic overview of key facts and timelines. Re-Deployable Linguistic Model Workflow – Lighthouse linguists developed models based on intimate knowledge of the language used within the datasets, then deployed them within proprietary search technology to sort documents into tiers based on the likelihood that they contained key information. Deposition Kit Bundle Workflow – By bundling deposition kit requests from the same jurisdictions and departments together, we could search across smaller collections of documents and take a deponent-agnostic approach. Previously Delivered Name Hit Workflow – We provided case teams with documents from previously delivered results, giving them an advanced start on deposition preparation while further reducing duplicative searching. These repeatable workflows significantly reduced the volume of searching and coordination required across matters and enabled Lighthouse experts to quickly zero-in on the exact documents needed—without wasting counsels’ time with redundant and unimportant documents. Critical Docs Found and Delivered Across Dozens of Matters and Hundreds of Kits Over the course of two years, Lighthouse experts prepared dozens of case teams for complex litigation and handled a deluge of competing deadlines, priorities, and ad hoc requests (totaling as many as 70 requests at a time). For the Joint Defense Group, this meant: Over 1,150 deposition kits across 24 matters, encompassing 245K unique documents Over 100 state overviews across 21 different jurisdictions, encompassing 80K documents For law firms representing individual defendants, Lighthouse provided an additional:150 deposition kits, encompassing 13K documents 30 defensive overviews across 20 jurisdictions, encompassing 6K documents 1.3K documents in response to ad hoc requests and trial support Each delivery was limited to essential information—including key themes and players in every jurisdiction, potential gaps in productions, lists of hot/sensitive documents and potential deponents, and key strategy documents—and avoided redundant and unimportant documents. The combination of innovative workflows and cutting-edge technology enabled Lighthouse to keep our team small and consistent throughout the engagement, so the entire effort was achieved by a handful of Lighthouse experts with institutional knowledge of every matter. Since this engagement, we have used the same workflows for other clients facing complex Multidistrict Litigation (MDL)—making Lighthouse key document identification one of the most valuable and scalable litigation technology solutions on the market today.

The Lighthouse team of SMEs applied their dedication to exemplary customer experience and unique strategy of marrying compliance, security, IT, and legal needs to help a global chemistry solutions and specialty material producer meet the ever-evolving security and compliance demands and challenges facing international manufacturing and regulations to effectively deploy Microsoft Purview across workstreams while preparing for needs and reducing costs. Global Leader in Chemistry Solutions Transforms Enterprise Data Protection with Microsoft Purview An international producer of commercial chemicals and specialty materials upholds a commitment to people safety and well-being as part of their core tenets. As cyber risks increased along with data volumes, the organization extended their commitment to safety to include the security of data accessed, produced, and stored within their enterprise. Now, the company has implemented a comprehensive data protection program using the entire Microsoft 365 Information Protection suite. After careful design, the team is piloting the solution before a global rollout. A Commitment to Physical and Digital Safety As one of the world’s largest acetyl products manufacturers and a top-tier producer of high-performance engineered polymers, the company supplies chemicals across major industries and for a variety of industrial and consumer applications. Over 10,000 employees in offices, technical centers, and 50+ manufacturing facilities work to realize a vision of improving the world and everyday life through people, chemistry, and innovation—with products that impact the lives of millions. For the organization, an operational approach rooted in well-being has always meant physically safe working environments for employees, and safe solutions for their customers and their communities. However, in this digital age, they have expanded their notion of safety to include data protection for employees, customers, shareholders, and the communities in which they operate. The company’s Chief Information Security Officer (CISO) notes that committing to data protection means a “higher level of assurance—making sure that our security controls keep pace with the threats that surround us every day and seek to exploit vulnerabilities in companies like us every day. You can’t stand still. You always have to evolve—you always have to get better, otherwise you’re devolving, and you’re getting worse, and becoming more vulnerable.” Advancing Data Protection with a Trusted Partner A few years ago, when the company decided to make the move to the cloud, they chose Microsoft 365 E5 and Microsoft Azure, building on their longstanding use of Microsoft technologies. Prior efforts to overhaul their data protection program had been unsatisfactory. However, with access to new Microsoft Purview capabilities, the Information Security team saw an opportunity to try again. They hoped to utilize the full breadth of the Microsoft 365 Information Protection suite including Information Protection Classification and Labeling, Data Loss Prevention (DLP), and Insider Risk Management solutions. Microsoft tapped Security Solutions and Advanced Specialization Designation-Information Protection and Governance Partner Lighthouse Global to lead the engagement for their ability to effectively understand complex compliance needs across IT, security, and legal departments. They hoped that together they could develop a solution to realize the investment they’d made in Microsoft 365, and to support their corporate commitment to safety for both employees and customers. “If you were to interview a bunch of companies, those who have actual, very successful DLP and data labeling programs typically have a hodgepodge of solutions that get melded together,” reflected the CISO, “and that’s where Lighthouse was successful…we’ve been able to leverage the investment…and get it to work, [and not] have to go spend more money to hodgepodge together a solution.” Developing a Comprehensive, Scalable Solution The Lighthouse team started by holding a series of working sessions to align the company’s vision and requirements and design the implementation approach. Using Microsoft Compliance Check, Lighthouse scanned the company’s environment to get an understanding of current state activity and sensitivity intelligence. The team also reviewed existing policies and approaches for the handling of sensitive data and data loss prevention to identify any areas of opportunity or gaps that could exist. From there, the combined teams were able to successfully design and configure a holistic data protection solution leveraging multiple Microsoft Purview products including Data Loss Prevention, Information Protection, and Insider Risk Management. Starting with data classification, the team defined the sensitive information types that needed to be identified. From there, they developed a set of sensitivity labels corresponding to the data protection policy. This set of classification techniques and labels were generated in the course of both Data Loss Protection and Insider Risk Management implementation, ensuring a comprehensive data life cycle protection program from content identification through insider threat analysis. Finally, the Lighthouse team supported the integration of the Microsoft products with the company’s third-party HR software to feed HR data into the Data Theft by Departing Employee Policy, enabling the creation of a truly end-to-end solution. Fulfilling a Mission of Security The company’s dedication to safety, security, and well-being across applications and contexts drove this project’s success. “Because we see security as part of our commitment to people and innovation, we take a uniquely holistic approach and have strong support all the way up to our board of directors,” says the company’s CISO. The CISO also credits Lighthouse’s unwavering commitment to partnership. “They helped us not only implement the technology and guide us through some of the critical points to consider as we implemented the technology, but also the process and decision points with data—which ultimately, in the end, actually worked,” they conclude. Now, with the design and implementation of the Microsoft Purview-based Data Protection program behind them, the organization’s information security team is focused on operationalizing the program through a series of pilots scheduled over the next year. Their ultimate goal is total, global implementation of the solution—and total, global protection for all employee and customer data. Corporate Case Studymicrosoft; big-datamicrosoft-365; data-privacy

Over the course of five months, Lighthouse delivered approximately 4,500 documents for review—out of the 2.3 million document review set—for a Fortune 100 health insurance provider. The Challenge Complex internal False Claims Act investigation 2.3M total documents for review Five-month timeline and tight budget Lighthouse Key Actions Provided curated weekly deliveries of the most important, inclusive documents for review—with no redundant or duplicative versions Compiled summary reports of each delivery (including highlights of high-priority information) to expedite counsel review Out of 2.3M documents, identified and delivered just the 4,500 documents counsel needed to review in order to conduct a comprehensive legal analysis Key Results for Counsel Immediately gained a grasp on the relevant facts and timelines hidden within a massive review set—without wasting time reviewing irrelevant information Quickly developed a deeper understanding of the underlying risks and nuances of the investigation, through consistent and iterative communication with Lighthouse search experts Confidently completed the investigation on time and within budget—even after large volumes of new data were added mid-investigation A Challenging Internal Investigation into False Claims Act Violations A Fortune 100 health insurance provider was pursuing an internal investigation involving potentially improper diagnosis practices undertaken by a wholly-owned provider group. The scope of the investigation included analysis of reimbursements processed across 20+ disease categories, potentially triggering False Claims Act violations. With 2.3M documents to review, it was unclear how the internal investigation would be completed within a constrained budget and timeline. Counsel reached out to Lighthouse for help. Lighthouse Hands Counsel the Keys to a Focused, Efficient Investigation A small team of Lighthouse information retrieval, legal, data science, and linguistic experts immediately began working with counsel to understand the specific allegations at issue, as well as catalogue the various sources of data that needed to be investigated. The team then designed and executed a battery of complex searches tailored to find instances of fraud or wrongdoing related to the allegations at hand. By staying in close communication with counsel, the Lighthouse team ensured that new search requirements and data sources were quickly integrated into the workstream to support fact development. On a weekly basis, Lighthouse delivered a streamlined set of documents responding to counsel’s evolving theory of the case. These deliveries also included a detailed breakdown of the categories of documents identified each week, descriptions of relevant internal processes and policies, and flagging of high-priority documents of particular interest to counsel. Each delivery was distilled down to only the most inclusive, non-redundant versions of relevant documents. In addition to keeping pace with ongoing requests and deliverables, the Lighthouse team also re-executed previous searches to address waves of new data rolling in midway through the engagement. A Faster and More Comprehensive Investigation Resolution Over the course of five months, Lighthouse delivered approximately 4,500 documents for review—out of the 2.3 million document review set. The Lighthouse deliveries encompassed everything counsel needed to know in order to resolve their investigation—and nothing more. The team accomplished this precision through deep subject matter expertise surrounding the allegations and underlying issues at play, consistent and effective communication with counsel, expert topic-based searching, and additional proprietary data analytics to remove unnecessary duplicative content. By the end of their short engagement with Lighthouse, counsel had developed a comprehensive understanding of the pertinent risk areas and confidently completed their investigation—on time and within budget. Corporate Case Studycase-study; corporate; corporation; ediscovery; fact-finding; document-review; investigations; kdi; key-document-identification; keyword-search; insurance-industry; analytics; ai-and-analyticsediscovery-review; ai-and-analytics; client-successCase-Study, client-success, Corporate, Corporation, eDiscovery, fact-finding, document-review, investigations, KDI, key-document-identification, keyword-search, insurance-industry, analytics, ai-and-analytics, ediscovery-review, ai-and-analytics

Lighthouse's proprietary AI technology solves a unique data deduplication challenge while migrating over 25 terabytes for an extensive healthcare system. Key Results In 5 months, Lighthouse migrated four databases—with 25 TBs of data—all while keeping the databases active for review and production for current matters. Leveraging our AI technology, Lighthouse created an innovative solution for a large volume of Lotus Notes files originally processed as HTML files by a legacy processing tool. This solution ensured that any new Lotus Notes files would deduplicate against the migrated data, regardless of the file type or the tool used for processing. A Challenging Data Deduplication Problem A large healthcare system had been hosting its data (over 25 TBs of data across four databases) on another vendor’s platform for nearly a decade. The company knew it was time to modernize its eDiscovery program with Lighthouse. In order to do so, all 25 TBs would need to be migrated over to Lighthouse for hosting and future processing. However, in addition to data migration, the company also had a unique deduplication challenge due to the previous vendor’s original processing tool. The company’s data had originally been processed with the vendor’s legacy processing tool—which processed Lotus Notes data as HTML files, rather than the more modern EML version. The prior processing of these files into an HTML format meant that whenever duplicate Lotus Notes files were added to the database and processed using a more modern processing tool, those EML files would not deduplicate against the older HTML files in the databases. With over half their data consisting of Lotus Note files processed by the older tool in HTML format, the company was concerned that this issue would significantly increase review cost and slow down review time. Thus, in addition to the overall migration process, the company came to Lighthouse with an unfortunate Catch-22: in order to modernize its processing and eDiscovery capabilities, it was losing the ability to deduplicate a majority of its data with each new ingestion. Lighthouse Migration Expertise Because of the volume of new clients moving to Lighthouse for eDiscovery support, Lighthouse has developed an entire practice group dedicated to data migration. This group is adept at creating customized solutions to the unique challenges that often arise when migrating data out of legacy systems. The team works closely with each client to understand the scope, types of data, challenges, and future needs so that the data migration process is seamless and efficient. The Lighthouse migration team quickly got to work gathering information from the healthcare company to start this process, paying particular attention to the Lotus Notes deduplication issue. Once all relevant information was gathered, Lighthouse worked with stakeholders from the organization to form a comprehensive migration plan that minimized workflow disruption and included a detailed schedule and workflow for future data. In the process, Lighthouse also developed a custom solution for the Lotus Notes issue using our proprietary AI technology. An Innovative Solution: Lighthouse AI Lighthouse’s advanced AI technology can create a unique hash value for all data, no matter how it was originally processed. The Lighthouse migration team leveraged this innovative technology to create a unique hash value for the Lotus Notes files that were originally processed as HTML files. That hash value could then be matched against any new Lotus Notes files that were added to the database by the company, even when those files were processed as EML files. With this proprietary workflow, the healthcare company was able to seamlessly move to Lighthouse’s eDiscovery platform, which was better equipped to serve its eDiscovery needs—without losing the ability to deduplicate its data. Set Up for Success In just five months, Lighthouse completed a seamless migration of the healthcare company’s data by creating a custom migration plan that minimized blackouts and kept all databases up and running. Importantly, Lighthouse also leveraged its proprietary AI to create an innovative solution to a complex problem, ensuring continued deduplication capability and reduced discovery costs. ‍ Corporate Case Studycase-study; ai; ai-and-analytics; ai-big-data; corporate; corporation; ediscovery; ediscovery-migration; prism; processing; project-management; healthcareediscovery-review; ai-and-analytics; client-successCase-Study, client-success, AI, ai-and-analytics, AI-Big-Data, Corporate, Corporation, eDiscovery, eDiscovery-Migration, Prism, Processing, Project-Management, Healthcare, ediscovery-review, ai-and-analytics

A prominent law firm leveraged a cloud-based software solution to increase efficiency and scale, resulting in significant costs savings. What They Needed A mid-sized East Coast law firm­—known for its expertise and experience in complex and high-stakes matters—was looking for new software to replace its in-house legacy technology. Their in-house tool did not provide the level of sophistication or throughput the team needed to continue to scale their work for their clients. In assessing their potential new partner, the firm required access to best-in-class technology, in particular Relativity and Nuix, as the firm’s employees were already familiar with these platforms. In addition, they wanted to leverage automation to have repeatable processes that would save both themselves and their clients time and money. ‍ How We Did It Lighthouse Spectra was selected for its simple and intuitive interface that allows users to internally manage client matters across best-in-class technology – including Relativity, Nuix, and even Brainspace. With Spectra, the firm can now start matters immediately, without having to go through the vendor solicitation and/or statement of work processes, creating real time savings. And the monthly subscription price for Spectra gave them more transparency around billing and greater cost control to help them stay within their budget. The onboarding and training processes were quick, due to the experience of the internal team coupled with the ease of use of Spectra’s. After the initial deployment of Spectra, the firm started processing client data through the tool immediately. They were able to get these matters through processing (Nuix) to review (Relativity) within a few hours, rather than an entire day or more, as was typical with their previous in-house solution. We can go from soup to nuts without having to reinvent the wheel each time. It is truly self-service. — Law Firm The Results Soon after onboarding, the firm took on a couple quick-turn and complex matters that they were able to handle more quickly due to the speed and scale of Spectra, as well as the support of Spectra team. In one instance, they received a request late in the work day that needed to be turned around within a short period of time. Prior to deploying Spectra, that would have taken some hands-on experience and a day’s worth of time. With Spectra, they were able to process it as soon as they received it and it was available for review within a few short hours. In another instance, the firm received a request with a pressing deadline where the document set consisted of approximately 95% foreign-language text. Quickly translating the text to English was imperative to firm’s success. To solve this problem, the Spectra team pointed the firm to a machine language translation tool that easily integrates with Spectra. By deploying the integrated translation service on the workspace, documents submitted for translation were loaded back into the workspace as easily as if it was performing a mass edit. This provided an easy solution for the firm for this particular matter, and now that it’s integrated, the feature is available to the firm on demand. By moving to Spectra, the law firm was able to leverage best-in-class technology, gain more transparency and control around the entire eDiscovery process, and create efficiencies and therefore, reduce costs for themselves and their clients. Leveraging Spectra, the law firm can now do more with less and scale their business to support their clients’ growing needs. ‍ Law Firm Case Studycase-study; ediscovery; self-service, spectra; spectra; ai-and-analytics; analytics; processing; tar-predictive-coding; technology-assisted-review; tar; law-firmediscovery-review; client-successCase-Study, client-success, eDiscovery, self-service, spectra, Spectra, ai-and-analytics, analytics, Processing, TAR-Predictive-Coding, technology-assisted-review, TAR, Law-Firm, ediscovery-review,

How They Did ItPenningtons Manches Cooper partnered with Lighthouse to deploy Spectra, which was implemented within three months from initial proof of concept to rollout with live matters. Primary areas of focus during the implementation were training, process design, and internal change management. The project began with roundtable sessions to fully understand the scope and ensure that deployment was customized to fit Penningtons Manches Cooper’s requirements, deliverables, and goals. And because Spectra is a cloud-based solution, there was no capital expenditure or additional IT resourcing required for implementation. This allowed for a flexible approach, fast implementation, and low ongoing maintenance for Penningtons Manches Cooper.Once the tool was initially implemented, the team at Penningtons Manches Cooper identified a suitable matter to be used in a proof of concept. Lighthouse trained key Penningtons Manches Cooper personnel on how to use Spectra, and together the two teams worked to create a scalable and repeatable workflow for particular work types. All items were recorded in a bespoke playbook, which fully documents Spectra’s capabilities and process as well as specific Penningtons Manches Cooper requirements.Next, Lighthouse provided training to the wider Penningtons Manches Cooper team on Spectra's built in analytics and redaction tools. Due to the simplicity and on-demand nature of Spectra, the team at Penningtons Manches Cooper was able to realize a 1 to 4-hour reduction in the time it takes to create a matter and upload data into Relativity. Further, Lighthouse developed a custom Relativity template. to ensure the user experience in Relativity is mirrored across matters and complements the firm’s workflows.Following the successful trial period, Penningtons Manches Cooper has identified and managed many other matters in Spectra with very little external support. Setup of each new matter has been reduced significantly, in some circumstances by up to 2-3 days, as there has been a significant reduction in the number of steps required to instruct external eDiscovery vendors, including no need to gather price proposals, no delay while vendors run conflict checks, and no need for any additional contract negotiation. As a consequence, each legal team was typically able to begin reviewing documents on the same day the data was received by the firm.In conjunction with the above, predictable and recurring billing practices were implemented and custom reports were developed around the firm’s matters and metrics. This, in turn, will allow Penningtons Manches Cooper to manage cost recovery and integrate billing for a more seamless and efficient process.The ResultsPenningtons Manches Cooper partnered with Lighthouse to roll out Spectra, which enabled their team to control the process from the very start and create efficiency and predictability of cost and process. By using Spectra, the team at Penningtons Manches Cooper was able to create matters and upload and process data quickly, all within a simplified and intuitive interface. The use of best-in-class technology, combined with repeatable process and in-house expertise, created a tangible benefit, ensuring eDiscovery and document review are completed with minimal cost, a savings which can be passed on directly to the client.

Lighthouse Spectra helps a considerable healthcare organization gain control, pricing transparency, and efficiency gains in the eDiscovery process. What They Needed A large healthcare organization was looking to solve their eDiscovery challenges around speed and cost. Specifically, they needed to increase their overall efficiency, and have more control over their matters with truly transparent and lower ediscovery-related costs. How We Did It Lighthouse Spectra was chosen to help achieve these key goals. Spectra is a self-service, on-demand eDiscovery tool with a transparent subscription-based pricing model. Spectra users can also access a full-time project management team at Lighthouse, whenever needed – all for one predictable price. Spectra onboarding was tailored to the users’ needs and focused on teaching users how to use Spectra itself, as well as when and how to use Brainspace, an analytics engine available inside the platform. Since Spectra is built with an intuitive interface, it only took a few short trainings over the course of a few weeks for the users to become comfortable using it. The Lighthouse team also ensured that Relativity and Spectra were customized to the organization’s specific needs. Our teams ensured that all customized permissions and views were set up within Relativity and worked with the organization to create custom Relativity templates to apply their standard coding pallets, rule-based coding propagations, pre-baked saved searches, standard views/layouts, imaging profiles, and more. Additionally, the Lighthouse team also assisted in building a continuous multi-model learning (CMML) workflow for their team to leverage within Spectra. Once set up was complete, the organization immediately started leveraging Spectra to process their data and run search terms as needed on a variety of diverse case types, including labor and employment cases, internal investigations, and OIG requests. The Results By moving to Spectra, the healthcare organization gained more control over their eDiscovery processes, created more efficient workflows, and achieved significant cost savings with transparent and predictable pricing. Since deploying the tool, the organization found that using the search and analytics capabilities of Spectra reduced the volume of natives to just 4.5% of the total hosted volume, minimizing the count of documents being reviewed by 95%. The custom Relativity template prevents the need to reinvent the wheel with each new matter and drive consistency across their portfolio. Further, the CMML workflow allows the organization to prioritize review of documents that are most likely to be responsive, as well as minimize the number of documents that go to review. Both of these enhancements allowed the organization to increase their overall speed from collection to production while lowering their overall eDiscovery-related costs. Through these new workflows and processes, the healthcare organization has achieved both defensibility and affordability and reduced review time from days to hours. This has resulted in an overall savings of $500K in their first year with Spectra.\ Corporate Case Studycase-study; corporate; corporation; ediscovery; self-service, spectra; spectra; ai-and-analytics; analytics; processing; tar-predictive-coding; technology-assisted-review; tar; healthcareediscovery-review; client-successCase-Study, Corporate, Corporation, eDiscovery, self-service, spectra, Spectra, ai-and-analytics, analytics, Processing, TAR-Predictive-Coding, technology-assisted-review, TAR, Healthcare, ediscovery-review

Cleary Gottlieb and Lighthouse save millions of dollars and thousands of hours in HSRs Second Request for Fortune 500 company. What They Needed A global Fortune 500 electronics company received an HSR Second Request from the Department of Justice (DOJ), with an extremely aggressive timeline to reach substantial compliance. They engaged Cleary Gottlieb (“Cleary”), a global technology-savvy and innovative law firm with extensive experience handling challenging Second Requests. After Cleary led negotiations with the DOJ to reduce the scope of the investigation, the client was faced with 3.3M documents to review—a significant subset of which included CJK language documents that would require expensive and time-consuming translation. To further complicate matters, the DOJ and Cleary remained engaged in ongoing scope negotiations, resulting in additional data being added throughout the project. Cleary knew that conventional TAR technology was not capable of evaluating a dataset with ever-changing review parameters. How Cleary and Lighthouse Did It CJ Mahoney, counsel and head of the eDiscovery and litigation technology group at Cleary, has extensive experience working on complex HSR Second Requests and has pioneered a number of different analytics-driven methods to reach substantial compliance in the past. Based on prior joint success in innovating new ways to use this technology to improve privilege analytics, CJ immediately saw the potential of Lighthouse’s proprietary AI technology for this challenge. Together, CJ and the Lighthouse data scientists developed a unique training workflow to achieve highly precise responsive prediction results on this challenging dataset. CJ secured the DOJ’s first-ever approval of this workflow with Lighthouse’s proprietary AI technology. Immediately after approval, responsive and privilege analysis and review began simultaneously, enabled by AI technology. For responsiveness, the teams utilized an active learning TAR workflow wherein subject matter experts reviewed a control set of randomly selected documents. After only a few training rounds, the system reached stability and began scoring the remaining dataset for responsiveness. A privilege classifier was built based on 20K previously confirmed privilege calls and applied to score all documents in the privilege workspace. The teams used a combination of the analytic results and privilege terms to identify potential privileged documents. All documents within this set that were scored as “highly likely to be privileged” were immediately routed to reviewers for review and privilege logging. Conversely, documents scored as “unlikely to be privileged” were removed from privilege review after Cleary’s attorneys verified the accuracy of the results using a random sample. Further, the teams used the privilege classifier to identify additional privilege documents that had not hit on privilege terms. As the timeline for substantial compliance approached, negotiations with DOJ regarding relevant timeframes and custodians continued, resulting in the near-constant addition and removal of documents from the dataset. The Lighthouse and Cleary teams managed the ever-changing dataset with ease using the Lighthouse technology and workflow developed by the teams. The Results Using a specialized TAR workflow leveraging advanced AI, the teams delivered highly accurate responsive classification, resulting in more than 500K (or more than 40%) fewer documents requiring further review and production to the DOJ, when compared to legacy TAR tools. By creating a smaller volume of documents requiring production, the amount of privilege and foreign language review was also lessened. For example, 120K fewer foreign language documents were included in the final responsive set compared to legacy TAR tool results. This reduction of review and translation saved approximately $1M alone. For the client, the smaller responsive set meant faster production turnaround times, lower overall costs, and risk mitigation through the decreased chance for inadvertent production of non-responsive documents. The Lighthouse and Cleary partnership resulted in the removal of 200K documents from privilege review beyond what could have been possible through conventional methods, leading to cost savings of $1.2M and time savings of 8K review hours. The team further mitigated risk to the client by identifying privilege documents that did not hit on standard privilege terms. The Cleary and Lighthouse partnership resulted in substantial compliance with the HSR Second Request, increased risk mitigation, faster document review, and remarkable savings for the client. Law Firm Case Studycase-study; antitrust; ediscovery; tar; tar-predictive-coding; law-firm; hsr-second-requests; investigations; mergers; ai-and-analytics; ai-big-data; artificial-intelligence; ai; acquisitions; analytics; predictive-coding; prism; privilege; privilege-review; tech-industryediscovery-review; antitrust; ai-and-analytics; client-success; lighting-the-path-to-better-ediscoveryCase-Study, client-success, Antitrust, eDiscovery, TAR, TAR-Predictive-Coding, Law-Firm, HSR-Second-Requests, investigations, Mergers, ai-and-analytics, AI-Big-Data, artificial-intelligence, AI, Acquisitions, analytics, predictive-coding, Prism, privilege, privilege-review, tech-industry, ediscovery-review, antitrust, ai-and-analytics

Lighthouse saves insurance giant millions of dollars during major technology upgrade. Key Actions Microsoft referred the Company to Lighthouse to resolve existing concerns from the Company’s IT and legal departments that were stifling their automation and transition process to Microsoft 365 (M365). Lighthouse held educational workshops on eDiscovery tools within M365, and devised a comprehensive plan for the compliance. Key Results Unblocked the M365 transition effort and enhanced the partnership between legal and IT. Compliance concerns were answered within M365, saving the company millions of dollars in retaining or updating legacy data management systems. What They Needed Legal Concerns Churn 11th Hour Nightmare for IT Department In 2017, a nationwide insurance giant initiated a transition from an on-premises Microsoft solution to a cloud-based M365 solution fueled by gain from cost, performance, and security improvements. Years later, and well past the intended launch date, the Company’s legal team suddenly halted the transition entirely due to concerns of M365’s eDiscovery capabilities, specifically, how M365 would handle the identification, preservation, and collection of email, instant messages, and files for the Company. The legal department insisted the company retain its custom-built archival solution until all compliance concerns were allayed. These demands put the IT department in an extremely tough spot after having already invested several years into the transition to M365. If forced to extend their aging, on-premises solution, the team would face substantial costs. To help unstick the implementation project, Microsoft suggested the Company engage Lighthouse to assist. Lighthouse immediately understood the legal team’s concerns and acted swiftly to address the Company’s insistence on exercising the transition to M365 with great caution, all while remaining vigilant of the Company’s receipt of hundreds of new legal matters monthly. The sensitive nature of data in this industry and the complex regulatory environment made the potential risk related to mismanagement very high. The process was intricate and complex, and required high-level integration to mitigate the significant risks that were specific to individual privacy regulations, such as the California Consumer Privacy Act (CCPA) and the European Union’s General Data Protection Regulation (GDPR). Hands-on Experience and High-touch Service Bridge the Gaps Lighthouse fielded a team of experts with direct experience in the same or similar roles as the various client stakeholders, ranging from IT to records management, corporate legal, and public affairs. This hand-selected team led a three-part process with their counterparts from the Company: Providing education on the eDiscovery aspects of M365 Analyzing current workflows and performance, and expressing their desired future state Devising a high-level design document for how relevant parties could conduct eDiscovery tasks in compliance with the requirements while using M365 The first two processes helped restore unity among stakeholders, while the design document delivered on the legal team’s concerns, including specified settings for a range of M365 applications and components, such as Exchange Online, SharePoint Online, OneDrive for Business, and Teams. The design document made room for process automation and/or custom workflows, as well as for third-party system integration (for compliance archive, legal hold, matter management, etc.). The initial project success led to a continuing relationship between the Company and Lighthouse, and over time Lighthouse has become a critical element in the Company’s ongoing M365 implementation and adoption journey helping them in charting a path forward. Corporate Case Studycase-study; big-data; cloud-migration; cloud; cloud-services; ccpa; corporate; corporation; data-privacy; data-protection; emerging-data-sources; information-governance; ediscovery; microsoft; gdpr; legacy-data-remediation; legal-holds; risk-management; insurance-industry; record-managementmicrosoft-365; data-privacy; information-governance; client-success; lighting-the-path-to-better-information-governanceCase-Study, Big-Data, Cloud-Migration, cloud, Cloud-Services, ccpa, Corporate, Corporation, Data-Privacy, data-protection, Emerging-Data-Sources, Information-Governance, eDiscovery, microsoft, gdpr, Legacy-Data-Remediation, Legal-Holds, microsoft, risk-management, insurance-industry, Record-Management, microsoft-365, data-privacy, information-governance

Lighthouse bridges internal gaps during technology overhaul and solves longstanding compliance issues for a German multinational healthcare manufacturer. Key Actions Lighthouse engaged company stakeholders in operational planning and received funding from Microsoft to devise and integrate a premium Microsoft 365 (M365) add-on to existing Purview Premium eDiscovery, which resolved an outstanding compliance need. Key Results The proof-of-concept achieved a zero-trust security model integrated with third-party software, and satisfied the barring of critical needs for the Company that centralized IT and legal departments after years of dysfunction. What They Needed Automating a transition to M365 commonly yields a clash between IT, legal, and compliance stakeholders if the decision to convert was spearheaded by IT and made without consulting legal and compliance teams. Typically, during planning or implementation of converting to M365, legal teams ask IT how the new platform will manage compliant and defensible processes, and if IT doesn’t have the answers, the project stalls. This was the situation facing a multinational manufacturing Company that engaged Lighthouse for help during the spring of 2020. At that time, the Company was several years into its M365 transition, and the legal teams’ requirements for adoption of native M365 compliance tools barred a complete transition. Pressure to adopt the tools escalated as M365 workloads for content creation, collaboration, and communication were already rolled out, creating an increasingly large and complex volume of data with significant degrees of risk. Lighthouse Responds to Need and Launches New Technology In partnership with Microsoft Consulting Services, Lighthouse organized a companywide M365 “reset,” hosting a three-day workshop to revamp the transition process and generate an official statement of work. The strategic goal was to streamline the stakeholders from litigation, technical infrastructure, cybersecurity, and forensics teams that previously failed to align. The workshop fielded critical topics geared to encourage constructive discussions between stakeholders and to strengthen departmental trust. The outcome of these discussions eventually enabled the company to move forward with critical compliance updates, including the collection and parsing of Microsoft Teams data, and the management of myriad files and email attachments. Lighthouse took stock of the current state, testing potential solutions, and arrived at a proof-of-concept for an eDiscovery Automation Solution (EAS) that augmented existing M365 capabilities to meet the legal team’s security requirements and remediate any performance gaps. Microsoft recognized the potential value of the EAS for the wider market, ultimately leading to Microsoft funding for the proof-of-concept. Inside the eDiscovery Automation Solution (EAS) Technology Azure-native web application designed to orchestrate the eDiscovery operations of an M365 subscriber through Purview Premium eDiscovery automation Maximized Microsoft Graph API “/Compliance/eDiscovery/” functions and other Microsoft API Simplified to Azure AD trust boundary, targeting the M365 tenant hosted within, and enabling full governance of identity and entitlement throughout Azure and M365 security features Benefits Achieved a zero-trust security model Authorized high-velocity, high-volume eDiscovery tasks without outside technology through automation and orchestration of existing M365 eDiscovery premium capabilities native to M365 Mobilized integration with third-party software included in the Company’s eDiscovery workflows Amplified workload visibility by automatically surfacing relevant Mailboxes, OneDrives, and other M365 group-based technologies dependent upon selected Custodians’ access Corporate Case Studybig-data; case-study; cloud-migration; cloud; cloud-services; cloud-security; corporate; corporation; data-privacy; emerging-data-sources; information-governance; ediscovery; microsoft; manufacturing-industry; risk-managementchat-and-collaboration-data; ediscovery-review; microsoft-365; data-privacy; information-governance; client-success; lighting-the-path-to-better-information-governanceBig-Data, Case-Study, Cloud-Migration, cloud, Cloud-Services, Cloud-Security, Corporate, Corporation, Data-Privacy, Emerging-Data-Sources, Information-Governance, eDiscovery, microsoft, manufacturing-industry, risk-management, chat-and-collaboration-data, ediscovery-review, microsoft-365, data-privacy, information-governance

Lighthouse's forensics experts found hidden clues missed during an internal investigation, proving a departing employee was stealing company data. Lighthouse Key Results By quickly engaging Lighthouse forensics experts: The company stopped proprietary and sensitive information from being disseminated and used by competitors. The company’s law firm was able to quickly take action against the employee, preventing any further malfeasance or damage. Investigation Overview Week 1 Day 1 – 4 — Employee uploads company data onto a personal Google Drive account over the span of four days. ‍ Day 4 – 5 — An internal investigation concludes that all company data has been deleted from the employee’s personal data sources and no further action is needed. However, the company’s outside counsel calls in Lighthouse forensics experts to perform a separate investigation for affirmation. ‍ Day 6 — Lighthouse forensics experts find evidence missed during the company’s internal investigation, indicating that the laptop provided to internal investigators was a “decoy,” and that the employee had actually transferred the proprietary company data onto an as-of-yet undisclosed laptop. Week 2–4 Outside counsel uses Lighthouse’s findings to file a restraining order against the employee and elicit a confession wherein the employee admitted they had downloaded the proprietary data onto a secret laptop—owned by another business. Week 6 Lighthouse forensics team is provided access to the additional laptop and the employee’s private Google Drive account. Although there is no company data stored on the drive, the Lighthouse team dives deeper and immediately finds that the employee had restored the previously deleted company data back to their Google Drive account, transferred it the secret laptop, and then deleted it again from the Google Drive account. These findings enable outside counsel to take additional remediating actions. Suspicious Activity by a Departing Employee Raises Alarm Bells During routine internal departing employee analysis, a global company was alerted to the fact that an employee had uploaded more than 10K files containing sensitive proprietary data to a personal Google Drive account. The company immediately launched an internal investigation and engaged their outside counsel. Over the course of the internal investigation, the employee admitted they had uploaded company data to their Google Drive, and then used an external hard drive to transfer that data onto a personal laptop. However, the employee avowed that all company data had since been deleted—which the company’s IT team confirmed by examining all three data sources. However, due to the sensitivity of the data, outside counsel wanted additional reassurance that the employee was no longer concealing proprietary company data. The law firm had previously relied on Lighthouse forensics experts for similar investigations and knew that they could count on Lighthouse expertise to find any hidden clues that would point to additional hidden data. Finding the Forensic Breadcrumbs Week 1 The Lighthouse forensics team received access to forensic images of the employee’s personal laptop and external hard drive within one week of the first suspicious upload. The team immediately noticed that the employee’s data tracks conflicted with the timelines and statements provided by the employee during the company’s internal investigation. Key Evidence Found by Lighthouse Forensics Experts The external hard drive used to transfer company data had not been plugged in to the personal laptop during the relevant time frame. File paths identified on the external hard drive (which show the file locations where data was downloaded upon connection) did not match those on the personal laptop provided to internal investigators. This evidence led the Lighthouse team to conclude that the laptop provided by the employee was not the laptop used to download company data—and that a different laptop with the stored proprietary company data existed but had not been disclosed by the employee. Week 2–4 A Lighthouse forensics expert provided a sworn declaration explaining the evidence found during the examination of the employee’s personal devices. The company’s law firm used this declaration to file a restraining order to stop the employee from continuing to steal or disseminate proprietary data. The law firm also used Lighthouse’s findings to elicit a confession from the employee, admitting that they had been secretly working part-time for another business, and had transferred the company’s proprietary data onto a laptop provided to the employee by that business. Week 6 Within two weeks of the Lighthouse forensics expert’s sworn declaration, the Lighthouse team was provided access to the laptop owned by the other business, as well as the employee’s personal Google Drive account. Lighthouse’s inspection of the Google Drive did show that all company data had been deleted, as had been confirmed by internal investigators. However, Lighthouse immediately went deeper into the Google Drive and found conclusive evidence that the employee had subsequently “restored” the deleted proprietary data just a few days after the internal investigation ended, in an attempt to continue with the data theft. Key Evidence Found by Lighthouse Forensics Experts Despite the fact that no company data was stored on the employee’s personal Google Drive account at the time Lighthouse received access to it, Lighthouse forensics experts went above and beyond to do a deeper forensic dive into the user activity log, email account, and internet searches stored on the Google Drive. That deeper analysis showed that: Two days after the internal investigation ended, the employee began conducting numerous internet searches for ways to “restore” deleted files on Google Drive. Two weeks later, the employee emailed a private IT company asking for help restoring deleted Google Drive files. One day after sending that email, thousands of files were restored to the employee’s Google Drive. Those restored files were once again deleted a few days later. Before the restored files were re-deleted, the employee downloaded some of the files containing company data to the “secret” laptop owned by another business. Keeping a Lid on Pandora’s Box The evidence found by Lighthouse forensics experts after their initial examination of the employee’s personal devices enabled the company’s law firm to take legal action against the employee less than one month after the first suspicious data upload. Within one day of being provided access to the employee’s personal Google Drive account, Lighthouse forensics experts were able to find exactly how and where the stolen proprietary and sensitive data was hidden. This enabled the company to permanently prevent any dissemination of that proprietary and sensitive data to competitors. ‍ ‍ Corporate Case Studycase-study; corporate; corporation; g-suite; forensics; investigations; collections; fraud-detection; red-flag-reporting; departing-onboarding-employeedigital forensics; client-successCase-Study, client-success, Corporate, Corporation, -G-Suite, digital forensics, investigations, collections, fraud-detection, Red-Flag-Reporting, Departing-Onboarding-Employee, digital forensics

Lighthouse experts distilled crucial information from millions of produced documents for a client's legal strategy during a Department of Justice investigation. Key Actions Lighthouse created 35 deposition kits by conducting two large-scale data investigations—and addressing multiple ad-hoc emergency investigations in the process—on an initial production set of six million documents, identifying the 4,100 most relevant items. Lighthouse adhered to a complex delivery schedule so the case team had time to prepare for each deposition. ‍ Key Results Counsel was well-prepared for 35 depositions using the deposition kits delivered by Lighthouse. Instead of spending time and review cycles finding they evidence, they used the bandwidth they saved to hone their legal strategy. ‍ Responding to a Fast-Moving Government Investigation, with a Merger on the Line When two of the largest publishing companies in the country entered a merger deal, the Department of Justice (DOJ) reacted with a large anti-trust investigation. Pursuant to an HSR Second Request, the companies produced a combined six million documents to the DOJ. In response, the DOJ sought to depose 35 individuals within a few months’ time. This left outside counsel with just two months to prepare for the defense of a massive potential merger, including intensive preparation for all 35 depositions. To do so, they knew they would need to find every shred of relevant information hidden within those six million documents—as quickly as possible. Executing a Plan for Better Legal Strategy When the law firm reached out to Lighthouse for help, our agile search team of analytic, legal, and linguistic experts immediately got to work, consulting with counsel to understand the specifics of the investigation, as well as the case team’s initial strategy for response. Using this background, the Lighthouse team mapped out a information search plan leveraging advanced volume reduction technologies and linguistic search models, delivering: Comprehensive deposition kits for all 35 deponents. Each kit was scheduled to be delivered well ahead of the corresponding deposition date, and included summaries of Lighthouse experts’ findings and highlights of notable documents and facts, in order to give counsel adequate time to prepare for each deposition. Key and relevant documents related to the DOJ’s anti-trust concerns and outside counsel’s defense strategies. These documents, provided on a rolling timeline, were uncovered by conducting two large scale data investigations: one to find all documents related to determining which publishers participated in or won the auctions, and another to find all documents necessary to facilitate the creation of an all-encompassing book auction timeline. Given the legal and analytic expertise of our specialists, Lighthouse search results often uncovered new areas of importance for the case team. When the case team responded to this new information with urgent follow-up search requests (with results sometimes needed in 24 – 48 hours), our team also boosted efforts to provide the requested information. Powering Counsel with Knowledge—and Time By partnering with Lighthouse, the case team stayed focused on preparing for depositions and crafting a response to the DOJ’s concerns to the merger, instead of conducting database searches and reviewing irrelevant or redundant documents. In just two months, Lighthouse found and delivered the 4,100 documents the case team needed, out of an initial population of six million documents. This included creation and delivery of 35 deposition preparation kits, all documents related to the case team’s strategy for responding to the DOJ’s antitrust concerns (delivered on a rolling basis), and results of six ad hoc case team investigation requests. All deposition kit and derivative search deliveries met or exceeded counsel’s delivery deadline expectations. Law Firm Case Studycase-study; document-review; ediscovery; fact-finding; kdi; key-document-identification; law-firm; hsr-second-requests; investigations; mergers; acquisitionsediscovery-review; ai-and-analytics; antitrust; client-successCase-Study, client-success, document-review, eDiscovery, fact-finding, KDI, key-document-identification, Law-Firm, HSR-Second-Requests, investigations, Mergers, Acquisitions, ediscovery-review, ai-and-analytics, antitrust

Lighthouse proprietary, technology-enabled strategy for finding key documents gives counsel a strategic advantage in a challenging HSR Second Request. Key Results In just three weeks, the Lighthouse team found the 1K most important documents out of an initial data population of 19M documents. Lighthouse experts began flowing key documents to the case team just three days after the initial kickoff meeting. Lighthouse saved counsel at least a month’s worth of preparation time for witness interviews and defense planning by efficiently finding the most important documents. A Mountain of Data and a Short Timeline A global technology company and their two outside counsel teams needed to quickly prepare a winning defense in a high-stakes, time-sensitive, Department of Justice (DOJ) Hart-Scott-Rodino (HSR) Second Request. To do so, they would have to identify and review all potentially damaging (or alternatively, helpful) documents within an initial data population of 19M documents. Finding the most important documents within that massive data volume—in less than one month—presented a Herculean task. A Proprietary Solution for Finding the Most Important Documents Lighthouse’s technology-enabled search strategy is led by information retrieval experts with decades of industry experience, who utilize robust search technologies that support large data volumes beyond industry-standard tools. Together, this combination of cutting-edge technology and data expertise quickly surfaces critical documents, streamlining legal analysis and case preparation for case teams. Handing Over the Keys to a Strategic Defense With no time to lose, Lighthouse TAR and review experts were able to whittle down the 19M documents to just over 990K responsive documents for production to meet substantial compliance. Simultaneously, Lighthouse experts quickly got to work finding the most important documents for the case team. Rather than relying on keyword culling, the Lighthouse team analyzed the data population and leveraged proprietary algorithms to safely reduce the universe to documents that contained the unique content the case team needed. From there, a team of six data retrieval experts leveraged proprietary search technology and institutional knowledge of the client’s data, gleaned from working with the company in a managed services capacity, to find key documents that were critical to the case team. Our experts used an iterative process and had weekly meetings with the case team so that they could instantly integrate counsel and witness feedback throughout the project, which helped yield more accurate search results. With this process, the Lighthouse team began flowing key documents to the case team just three days after the initial kickoff meeting. Over the course of the next three weeks, the Lighthouse team provided a total 1K key documents (out of a 990K responsive documents) in eight rolling deliveries. By gaining immediate access to these documents and eliminating the need for time-consuming and costly manual review, Lighthouse saved the team at least a month’s worth of preparation time for witness interviews and defense preparation. Law Firm Case Studyantitrust; case-study; document-review; ediscovery; fact-finding; kdi; key-document-identification; tar; tar-predictive-coding; law-firm; hsr-second-requests; investigations; mergers; acquisitionsediscovery-review; ai-and-analytics; antitrust; client-successAntitrust, Case-Study, document-review, eDiscovery, fact-finding, KDI, key-document-identification, TAR, TAR-Predictive-Coding, Law-Firm, HSR-Second-Requests, investigations, Mergers, Acquisitions, ediscovery-review, ai-and-analytics, antitrust

Lighthouse red flag report prevents proprietary data from being taken by departing employee. Key Actions A global company partnered with Lighthouse to create a proactive departing employee program to prevent data loss and theft. Lighthouse forensics experts prepared Red Flag Reports for every departing employee that fell within a specific category of employees. Each report outlined the risks associated with the departing employee based on a skilled forensic examination of their activity and data. Soon after implementing the program, a Lighthouse Red Flag Report alerted the company to suspicious activity by a departing employee indicating a high risk for data loss. Key Results Because of Lighthouse’s analysis and quick response, the company was able to: Prevent sensitive data from being disseminated outside the company. Avoid costly litigation associated with proprietary data loss. Reevaluate the departing employee’s severance package due to breach of contract, resulting in additional cost savings. ‍ What They Needed A global company was dealing with an increased risk of data loss and theft from departing employees. The company retains large volumes of proprietary data spread across their entire data landscape. Much of that data is also highly sensitive and would create a competitive disadvantage for the company if it were to end up in competitors’ hands. The company was also facing a higher volume of employee turnover—especially within roles that had access to the company’s most sensitive data (e.g., company executive and management roles). The company was concerned that these factors were creating a perfect storm for data theft and loss. They realized they needed a better system to catch instances of proprietary data loss before any data left the company. Company stakeholders reached out to Lighthouse because they knew our forensics team could help them build a proactive, repeatable solution for analyzing and reporting on departing employee activity. How We Did It Lighthouse forensics experts worked with the company to create a custom departing employee program for data loss prevention. With this program, Lighthouse experts prepared a Red Flag Report for every departing employee that fell within specified high-risk categories (e.g., employees above a specific seniority level, or employees that had access to highly sensitive company data, etc.). Each Red Flag Report was prepared by a Lighthouse forensics expert and summarized the data theft risk associated with the underlying employee. Every report contained: A high-level summary of the risk of data theft presented by the employee. A collection of attachments with highlights and comments by the Lighthouse forensics examiner (for example, a list of files stored in an employee’s personal cloud storage account, with an explanation of why that activity may indicate a higher risk of data theft). A forensic artifact categorization with associated risk ratings (e.g., if there were no suspicious search terms found during a scan of the employee’s Google search history, the examiner assigned that category a lower risk rating of “1”). Recommended next steps, with options for substantiating high-risk employee behavior. Reports were delivered to a cross-functional group of company stakeholders, including IT, human resources, and legal groups. The Results The Lighthouse program very quickly paid off for the company. Soon after initiation, Lighthouse escalated a Red Flag Report for a departing employee that showed a high risk of data loss. Specifically, the Lighthouse forensics examiner flagged that the employee had connected two different external thumb drives containing sensitive company data to their laptop. This activity was flagged by the Lighthouse forensics examiner as high risk because: The employee had already been directed by the company to return any device that had corporate data saved on it; and The employee had previously indicated that they didn’t have any devices to return. As soon as Lighthouse escalated the Red Flag Report, company stakeholders scheduled an interview with the employee. This interview resulted in the employee admitting that they had taken corporate data with them, via the two thumb drives. Because Lighthouse was able to quickly flag the employee’s suspicious activity, the company was able to retrieve the thumb drives before the proprietary data was disseminated to a competitor. The company was also able to reevaluate the employee’s severance package due to the breach of company policy, resulting in a significant cost saving. Even more importantly, the company now has a proven, proactive, and customized solution for preventing data loss and theft by departing employees—implemented by Lighthouse’s highly skilled forensics team. ‍ Corporate Case Studycase-study; corporate; corporation; forensics; investigations; collections; fraud-detection; red-flag-reporting; departing-onboarding-employeedigital forensics; client-successCase-Study, client-success, Corporate, Corporation, digital forensics, investigations, collections, fraud-detection, Red-Flag-Reporting, Departing-Onboarding-Employee, digital forensics

Imagine you are an attorney general suing a pharmaceutical company that manufactures a synthetic opioid spray and you're alleging racketeering. You've received 1.5 million documents in production and your team has three weeks before the next case management conference. Traditional review would have meant that you would spend months and likely millions of dollars to interrogate your data. Let's see what IQ Answers can do in the next several minutes. First off, I'm gonna just ask general questions within my dataset to get a sense of what was going on with this product and who it was prescribed for and what was happening with those prescriptions. So I'm typing a question about that generally to see what that returns for me. You'll notice here at the end of the query that you get an overall answer, but you also get topics surfaced for you along with citations that match each of these topics that highlight the particularly important parts of the document that can help lead you to the conclusions that are surfaced for you with IQ Answers. So here we see that this was a spray that was approved for breakthrough cancer pain and that that was the FDA intent of the approval. But what I'm also seeing in these topics that is being surfaced for me is that a lot of off-label prescription for non-cancer pain is apparently going on with this product. And I can tell that from these particular documents that I've clicked on here along with the citations where I'm seeing a hundred plus prescriptions a month that are off label. Well that's interesting. Now I'm also seeing that the salesforce has been explicitly instructed to prioritize high volume opioid prescription writers regardless of their specialty and regardless of what's happening with that prescription. And I can click into the documents and see that the volume is what is driving here. And then I also can look and see that there's been awareness internally that the approved indication from the FDA is not actually matching the patient population. So that's very interesting. It seems like we definitely have some use here that it was not intended, and I could figure that out in about 30 seconds in IQ Answers. I have the foundation. So, if the drug is approved for cancer pain, but the company is deliberately targeting non-cancer patients, that's definitely a gap from the FDA approval. And so, let's understand how they're going about making this happen. How are they getting doctors to keep writing these prescriptions? I'm going to go ahead and ask an additional question of IQ Answers to figure out what they're doing to get this volume of prescriptions written. And in particular, I noticed that there was something about speaker engagements that they were doing with doctors. So I'm going to ask about those speaker engagements. And you'll see here that basically the speaker engagement program was a way for them to pay doctors for prescriptions. I can see that they were tracking the ROI of those speaker payments as compared to the amount of prescriptions a doctor was writing. And I can also see from the documents and the citations that if they did not feel that a doctor was writing enough prescriptions, they were no longer part of these speaking events and, and thus paid through these speaking events. I also see these speaking events are not really genuine educational activity and it looks like the company was aware of this and was terminating doctors when they were not writing enough prescriptions to satisfy the amount of money that they were giving the doctors. So this all does not look very great for them in terms of how these programs were essentially bribery of these doctors. So if they're bribing the doctors, that still doesn't answer for me what's happening with the insurance companies. The product costs thousands of dollars a month and insurance companies wouldn't just approve that without any questions. So Vantage clearly needed a way to get these insurers to pay. Let me ask about that. I'm gonna type in a question about their programs to deal with the insurance companies and the way in which they instructed their employees. All right, so now I see in my answer that they had created what was called a Vantage Reimbursement Center. That was essentially their way of tracking the approvals from the insurance companies. And it looks like from the topics that's being surfaced here, that they were also using it as a vehicle to instruct their staff on how to deceive the insurance companies. In particular, here you can see in this document where they talk about using particular types of language, lingo, or diagnoses to get the insurance companies to approve the use of these drugs for the written prescriptions. But it also looks like they know that these are false statements that are being made when they are talking with the insurance companies. You can see here they're tracking and they want a 100% authorization success rate. So they're essentially giving all of their employees the way to beat the system, to cheat the insurance company, and to get them to approve something under false pretenses. So I now have two parallel fraud streams that are emerging here. One to bribe doctors to write prescriptions, one to deceive insurance companies to pay. For a RICO case, I also need to know that this was coming directly from management and from the top. So let's see if I can find that out in the dataset and ask a question about that to help me win my conviction. Alright, so now I'm gonna ask in particular, what evidence do we have that the CEO knew about this speaker program and this insurance program? Now that I'm asking this of the documents, what I can see here that is surfacing is that in fact the CEO did have direct knowledge and there's multiple documents that substantiate this. And in particular you can see this one here where he personally directs a speaker fee increase that is tied to prescription volume. In this document, you would have him talking about different tiers of speaker programs and that someone is no longer going to be invited to be a part of these speaker programs if they are not writing a certain amount of prescriptions. There's one here that surfaces a document where a hundred percent of the authorization success rate is something that he is directing and he's also calling for in the data. You also see here that I have additional follow up questions that I can be asked that are being surfaced by IQ Answers that will help me investigate and interrogate each one of these particular questions and the information I found up in these questions. But overall, it looks like I, in a matter of minutes, have been able to find key documents and important fact timelines that help me substantiate my case and know that I have a good chance of success moving forward and make me feel set up really well for my next case management conference.

Hi, Karl. Hi Stacy. How are you doing today? Good. It's good to see you. Good to see you too. Thanks for spending a little time with us today. Why don't you start out by telling us a little bit more about your role at Kirkland & Ellis and the types of matters that you handle? I'm a partner at Kirkland. My practice really involves the intersection of traditional discovery, preparing documents for production, meeting discovery obligations, and the like, and preparing case strategy, digging into the facts, understanding what those documents all show largely in the mass tort context. So large, very high-value litigation where facts and understanding the facts is really important. Well, so you just talked a little bit about case strategy, so I'd love to hear more about how you've used IQ Case Strategy, As part of litigation, it's really important that we understand what's in the materials, especially early on in the case. It's important to develop case strategy that's based on the actual evidence, based on the actual facts. And then we've, for a long time, been partnering with the Lighthouse team and members of the Lighthouse team to help us weed through, cull through the vast amount of material that often gets collected and produced in a case to really focus on what matters. It's important to be able to cull through those and really develop case themes, understand how particular issues are addressed in the documents, and then really leverage that information to be able to build out case themes, case arguments, develop our strategy for the case. And I think you've used IQ Case Strategy for depo prep in the past. We've actually, in a number of our cases, have really been able to leverage the deposition preparation to have our team focus on the substance of the case. Not spending time reading through a hundred thousand documents produced for custodian, but, focusing on the 100, 200, 300 that really matter the most for that person. That allows our team to focus on the substance as opposed to trying to figure out where in the vast universe of documents out there, that information is. That's great. So maybe touch a little bit on how AI is changing how you handle your client work at Kirkland. Yeah, it's a really exciting time, I think, for the entire field. I literally started in 2010 working on deposition prep, where I had partners who printed off tens of thousands of documents on hard paper and we would sift through them, flipping through them, and putting sticky notes on them. Since then, we've come a long way. The technology we have, especially with the evolving AI technologies, allows us to do more. More work and more effectively. Right by, early on in a case, understanding what the materials are, what the issues are, what the evidence shows, what the document shows, we're able to build out better case strategies that help our clients achieve their goals in litigation. So now that we've rolled out the full suite of applications under LighthouseIQ, I'm curious, beyond IQ Case Strategy, do you see yourself using some of the other tools that we've rolled out? We really do. We've really appreciated the Lighthouse IQ Case Strategy system and method for allowing us to do our work most efficiently and effectively for our clients. That said, I think with the advent of all the AI technologies and the implementation of those into the Lighthouse suite of tools that we've been talking about, we're really, really excited to leverage those materials and do more with them. In particular, the IQ Answers. We're really excited to, to try out on our next case to really help our teams and allow our teams to leverage the information that we're able to glean most quickly from, again, a large set of documents. That's great. Karl, thank you so much for spending time with us today. We really appreciate your partnership. Absolutely. Thank you, Stacy.

Hear how Eric Wieder and his team at Baker Botts uses LighthouseIQ get more control over when and how we review data.

Hear how Bryan Marra of Arnold & Porter uses IQ Case Strategy to get to insight faster. Hi Bryan. Hey Stacy. Good To see you. Why don't we start out by just sharing a little bit about your role at Arnold & Porter and the, the types of matters that you handle? So my practice focuses on merger investigations, the second requests, antitrust conduct investigations from the government and also merger litigation and other antitrust litigation. My specialty is really driving the eDiscovery process, um, helping clients use technology to drive more efficient outcomes for them in eDiscovery and complying with these government requests.Why don't You share a little bit about your use of LighthouseIQ, how it's helped you with your clients? Sure. So I used IQ Case Strategy. It was an antitrust litigation and we were facing trial in federal and state court in a matter of months. It was a very aggressive deposition schedule. We had over 30 depositions coming up in a matter of weeks, and we had a production population of over 23 million documents to look through.So how are we gonna tackle that? We could have done this the old fashioned way, hired an army of contract attorneys or used firm attorneys to basically sift through millions of documents, maybe use search terms to winnow it down, but then look at the results. It would've taken a long time, but instead we used IQ Case Strategy. So we worked with a small team of experts, linguists technologists, and we trained them on basically what they were looking for in terms of documents, what the case themes were about the witnesses. And they started delivering documents to us in a week. They kept up a weekly schedule basically to keep up with the depositions.They ultimately did depo prep kits for over 30 depositions and it worked well. I mean, out of 23 million documents, they surfaced thousands of the key documents and they surfaced them early enough for us to get insights and use them for prep in advance before the depositions. You mentioned using linguists and our Lighthouse team. How did pairing Lighthouse expertise with our LighthouseIQ technology help have a better outcome for you? The team we worked with, they basically developed classifiers and linguistic models, uh, using your technology to essentially identify key documents and it ended up being a really a seamless process. I mean, we thought the documents they came up with were relevant and were key documents and we thought it was an excellent use case.Maybe share a little bit what was the biggest impact of using LighthouseIQ? I mean, it was definitely the cost and time saving that was involved. I mean, we basically avoided having to do a massive document review effort, which would've cost the client a lot of money. And so by using this technology, we were able to short circuit having to do that and do things in a more efficient and focused way to get actionable intelligence that we could use, um, right away with our depositions and for the litigation strategy. So now that we've rolled out other applications, I know you've used some of them, but what are your overall thoughts of like using LighthouseIQ more broadly in the future? Yeah, I definitely see use cases to use more of LighthouseIQ technology.I mean, how I use AI is really to make eDiscovery more efficient for my clients to save money, to do things in a more focused way. And I can see use cases for IQ Answers. I'm, I'm looking forward to using that application for early case assessment to help clients find out what's in their documents at an early point to help focus the case strategy ahead of time. Maybe share a little bit more about how early insights really helps you and your teams. Definitely. So in merger investigations, finding out what's in your documents at an early point can be crucial.Oftentimes you produce like a million documents to the government and you don't really know what's in them ahead of time. But if you can know what's in your documents at an early point, it can really help you drive case strategy ahead of time. I Know you've used it on some large matters, but think about how, how would you use it on small matters or how you're thinking about the use of AI in the future. So I think size isn't really the main factor. I mean, clients are always looking to save money and they're always looking to find out more intelligence about their documents at an early point. And I think even in smaller matters, smaller investigations, this could be a way to see what's in your documents at an early point to drive case strategy.I look forward to using IQ Answers to do that, what I've seen of it that looks great and, and I think will help me in my practice for my clients. That's Great. Why don't you share a little bit about how you think about the use of AI in your practice? Really, AI helps you move from broad-based review solutions towards more focused actionable intelligence. And that's what we're trying to do in my practice to help clients use technology to find out more about their documents ahead of time to get through, you know, what would've been a massive costly undertaking much faster and more efficiently by using ai. And that's how I see my role is sort of being someone that uses this technology to drive these outcomes for my clients.Thank you so much for your time, Bryan. Really a pleasure to have this conversation with you. It was great speaking with you.

In every matter, there's a moment that changes everything. The moment information turns into understanding the moment, every decision becomes clearer, faster. The moment you can act with confidence, get to that moment quicker with LighthouseIQ, where intelligence meets performance. Hello and welcome. I'm Stacy Ybarra, Chief Marketing Officer at Lighthouse. Earlier this year we introduced LighthouseIQ, our eDiscovery platform, designed to bring earlier insight and greater control to the discovery lifecycle. Today you'll hear directly from clients putting that approach into practice. You'll also see how it helps legal teams stay flexible, whether they're managing complex matters today, or planning for how review platforms will evolve. To ground in how LighthouseIQ was built to support that flexibility, I'll turn it over to Iram Arras, our head of product and engineering. Iram. Thanks, Stacy. I'm Iram Arras, Senior Vice President of Product and Engineering at Lighthouse. My role is to make sure what we build actually works in the real world - not just in a controlled demo, but on live matters at scale when timelines are tight and consequences are real. Before joining Lighthouse, I spent time at Microsoft where the teams I worked with built Purview eDiscovery, which is used by many of the largest organizations in the world. That experience shaped how I think about this space. Legal technology has to be scalable, dependable, and defensible, and designed for pressure. Otherwise it doesn't matter how impressive it sounds. Since LighthouseIQ launched, a lot of questions I've been hearing are less about what it is and more about how it's different and why it was built the way it was. Lighthouse has been working with large language models and applied AI well before this technology became a headline. That gave us time to focus on the work behind the scenes, tuning models to handle real world scale, dialing in precision through continuous refinement, and building the safeguards that make them trustworthy in practice. The result is a platform teams can rely on day in and day out. From a product perspective, LighthouseIQ is a purpose-built eDiscovery intelligence platform with two core parts: IQ Applications that address specific high value workflows and IQ fabric, which is the foundation that weaves everything together. The goal is straightforward, help legal teams understand their data faster, make better decisions and control costs without compromising quality or defensibility. Today LighthouseIQ includes four core production grade applications designed for scale, accuracy, and consistency. Each built specifically for real eDiscovery workflows and already in use on complex matters. They are: IQ Answers, which allows teams to ask direct questions of their data and get immediate reliable answers; IQ Case Strategy helps translate insight into action; IQ Review delivers high quality relevance and classification across large data sets with speed and repeatability; IQ Priv provides precise privilege identification and logging, giving teams confidence in their outcomes. Underneath all of this is IQ Fabric, which brings together three core capabilities: Processing that standardizes data at massive scale while preserving fidelity; Cognition that helps interpret content and surface meaningful insights; Orchestration that connects workflows, tools and teams so work moves forward smoothly. Together these capabilities turn raw data into real action at scale and speed modern eDiscovery demands. That's the thinking behind LighthouseIQ and how we're seeing it used today. To illustrate how this works practice, Stacy sat down with Bryan Marra from Arnold & Porter to talk about how they use LighthouseIQ. Hi Bryan. Hey Stacy. Good to see you. Why don't we start out by just sharing a little bit about your role at Arnold & Porter and the types of matters that you handle? So my practice focuses on merger investigations and second requests, antitrust conduct investigations from the government and also merger litigation and other antitrust litigation. My specialty is really driving the eDiscovery process - helping clients use technology to drive more efficient outcomes for them in eDiscovery in complying with these government requests. Why don't you share a little bit about your use of LighthouseIQ and how it's helped you with your clients. Sure. So I used IQ Case Strategy. It was an antitrust litigation and we were facing trial in federal and state court in a matter of months. It was a very aggressive deposition schedule. We had over 30 depositions coming up in a matter of weeks and we had a production population of over 23 million documents to look through. So how are we going to tackle that? We could have done this the old fashioned way - hired an army of contract attorneys or used firm attorneys to basically sift through millions of documents, maybe used search terms to winnow it down, but then look at the results. It would've taken a long time. But instead, we used IQ Case Strategy. So we worked with a small team of experts, linguists, technologists, and we trained them on basically what they were looking for in terms of documents, what the case themes were about the witnesses, and they started delivering documents to us in a week. They kept up a weekly schedule, basically, to keep up with the depositions. They ultimately did depo prep kits for over 30 depositions and it worked well. Out of 23 million documents, they surfaced thousands of the key documents and they surfaced them early enough for us to get insights and use them for prep in advance before the depositions. You mentioned using linguists and our Lighthouse team. How did pairing Lighthouse expertise with our LighthouseIQ technology help have a better outcome for you? The team we worked with, they basically developed classifiers and linguistic models using your technology to essentially identify key documents and it ended up being a really a seamless process. We thought the documents they came up with were relevant and were key documents and we thought it was an excellent use case. Maybe share a little bit, what was the biggest impact of using LighthouseIQ? It was definitely the cost and time saving that was involved. I mean, we basically avoided having to do a massive document review effort, which would've cost the client a lot of money. And so by using this technology, we were able to short circuit having to do that and do things in a more efficient and focused way to get actionable intelligence that we could use right away with our depositions and for the litigation strategy. So now that we've rolled out other applications, I know you've used some of them, but what are your overall thoughts of using LighthouseIQ more broadly in the future? I definitely see use cases to use more of LighthouseIQ technology. I mean, how I use AI is really to make eDiscovery more efficient for my clients - to save money, to do things in a more focused way. I can see use cases for IQ Answers. I'm looking forward to using that application for early case assessment to help clients find out what's in their documents at an early point and to help focus the case strategy ahead of time. Maybe share a little bit more about how early insights really help you and your teams. Definitely. So in merger investigations, finding out what's in your documents at an early point can be crucial. Oftentimes you produce like a million documents to the government and you don't really know what's in them ahead of time, but if you can know what's in your documents at an early point, it can really help you drive case strategy ahead of time. I know you've used it on some large matters, but think about how would you use it on small matters or how you're thinking about the use of AI in the future. So I think size isn't really the main factor. Clients are always looking to save money and they're always looking to find out more intelligence about their documents at an early point. And I think even in smaller matters, smaller investigations, this could be a way to see what's in your documents at an early point to drive case strategy. I look forward to using IQ Answers to do that. What I've seen of it, it looks great and I think will help me in my practice for my clients. That's great. Why don't you share a little bit about how you think about the use of AI in your practice? Really, AI helps you move from broad-based review solutions towards more focused actionable intelligence and that's what we're trying to do in my practice - to help clients use technology to find out more about their documents ahead of time to get through what would've been a massive costly undertaking much faster and more efficiently by using AI. And that's how I see my role, is sort of being someone that uses this technology to drive these outcomes for my clients. Thank you so much for your time, Bryan. Really a pleasure to have this conversation with you. It was great speaking with you. Wow, that was a great conversation. What Bryan described is a model where intelligence comes first, giving your team flexibility before committing to full review. Iram, help us understand how LighthouseIQ was designed to support that approach as platforms continue to evolve. Customers are rightly asking two related questions: What happens with the retirement of Relativity Server in 2028? How will they reduce risk when changing something as central as a review platform? Our answer is straightforward. We reduce this risk by moving intelligence earlier in the lifecycle. So the choice of review platform matters far less than it does today. LighthouseIQ is designed to manage this transition seamlessly. It does not force workflow changes and it does not require customers to retool how they operate. Matters continue to move through LighthouseIQ without a dependency on Relativity Server because the platform is not tied to any single review tool or even to the assumption that documents must be reviewed in a traditional system at all. That flexibility is deliberate. Customers can choose how and where they review documents, if they choose to review them. LighthouseIQ sits above those decisions, providing consistent intelligence regardless of the tools underneath. And to be clear, we remain fully committed to Relativity and will continue to offer RelativityOne as a review option when it's the right fit for a matter. What fundamentally changes is where value is created. That shift is what gives customers more control, more optionality, and far less risk as the ecosystem evolves. With that context, it's more meaningful to hear how this plays out in practice. So next you'll hear directly from Eric Wieder at Baker Botts about how LighthouseIQ is helping their teams navigate these changes and deliver better outcomes on real matters. Hi Eric. Thank you so much for joining us today. I'd love to just hear a little bit about your role at Baker Botts and the type of matters that you oversee. Absolutely. So I am Senior eDiscovery Council at Baker Botts. I've been working in the eDiscovery industry for over 20 years and my role is across the spectrum of the EDRM from collection through production with a focus on review and I've managed many large second requests, multi-district litigation, both civil and criminal cases. I wanted to start out talking about Relativity and the announcement of Server going away in January, 2028 and curious, how does that operationally affect your team and how you think about eDiscovery? So in terms to the transition from Server to RelOne, we're not looking to replace Relativity. We're looking for an AI platform that will extract intelligent data from our clients and seamlessly integrate into Relativity for the benefit of our matter teams. This is what LighthouseIQ does. So tell me a little bit more, expand on that, on how LighthouseIQ will help you with that transition. LighthouseIQ gives us more control over when and how we're going to review the data. It used to be you collect the data and you review it linearly. LighthouseIQ gives us the chance to break it up into parts and review in a prioritized way to the benefit of the case. You've used IQ Priv for some of your cases. Can you share a little about that experience and how it's helped you? Yeah, so we have used the GenAI component of IQ Priv to assist us with our privilege log. As you know, the burden of preparing a privilege log is costly and expensive for our clients. We've been able to use the GenAI tool to more efficiently review documents and then perform a heavy QC so that we could submit a log that is less likely to be contested by the regulators. I understand you've used IQ Answers for some of your matters. Can you just elaborate a little bit more on how you've used it and how it's helped support your team? We have used IQ Answers on two different matters. One a second request, a second one a criminal litigation. In both instances, IQ Answers was very helpful in getting to the question of particular issues quicker than you would normally do just searching a Relativity database. Using an iterative process, we were able to get to the answer for the matter team in a short amount of time. Eric, it's been great spending time with you today. Thank you so much for sharing your experiences with us. Really appreciate you and appreciate the partnership with Lighthouse and Baker Botts. My pleasure, thank you. Wow, another insightful conversation. What Eric just described reinforces that this isn't about swapping platforms, it's about moving intelligence earlier so your teams are much less dependent on the review tools, or norms, at any one point in time. Iram, as you look ahead to 2028 and beyond, bring this all together for us. What does this shift really mean for legal teams as we look ahead to the future? Thanks, Stacy. What it comes down to is when legal teams get clarity. What you heard from Eric around IQ Answers is about getting meaningful insight earlier, while there's still time to shape strategy and make better decisions. When teams understand their data sooner, they can narrow scope, focus on what actually matters, and move forward with a far more confidence. That early clarity changes everything that follows. Decisions are more deliberate, costs are easier to control, and teams spend their time acting on insight instead of searching for it. That's the value of early intelligence and that's the direction we're building toward with LighthouseIQ. Hi Karl. Hi Stacy. How are you doing today? Good. It's good to see you. Good to see you too. Thanks for spending a little time with us today. Why don't you start out by telling us a little bit more about your role at Kirkland & Ellis, and the types of matters that you handle? Yeah, I'm a partner at Kirkland. My practice really involves the intersection of traditional discovery, preparing documents for production, meeting discovery obligations and the like, and preparing case strategy, digging into the facts, understanding what those documents all show largely in the mass court context. So large, very high-value litigation where facts and understanding the facts is really important. So you just talked a little bit about case strategy, I'd love to hear more about how you've used IQ Case Strategy. In litigation, it's really important that we understand what's in the materials, especially early on in the case. It's important to develop case strategy that's based on the actual evidence, based on the actual facts. We've, for a long time, been partnering with the Lighthouse team and members of the Lighthouse team to help us weed through, cull through, the vast amount of material that often gets collected and produced in a case to really focus on what matters. It's important to be able to cull through those and really develop case themes, understand how particular issues are addressed in the documents, and then really leverage that information to be able to build out case themes, case arguments, develop our strategy for the case. And I think you've used IQ Case Strategy for depo prep in the past. Yeah, and we've actually in a number of our cases, really been able to leverage the deposition preparation to have our team focus on the substance of the case. Not spending time weaving through a hundred thousand documents produced for a custodian, but, focusing on the 100, 200, 300 that really matter the most for that person. It allows our team to focus on the substance as opposed to trying to figure out where in the vast universe of documents out there that information is. That's great. So maybe touch a little bit on how AI is changing how you handle your client work at Kirkland. Yeah, it's a really exciting time, I think for the entire field. I literally started in 2010 working on deposition prep where I had partners who printed off tens of thousands of documents on hard paper and we would sift through them, flipping through them and putting sticky notes on them. Since then, we've come a long way. The technology we have, especially with the evolving AI technologies, allows us to do more, more work and more effectively. Early on a case understanding what the materials are, what the issues are, what the evidence shows, what the document shows. We're able to build out better case strategies that help our clients, achieve their goals in litigation. So now that we've rolled out the full suite of applications under LighthouseIQ, I'm curious, beyond IQ Case Strategy, do you see yourself using some of the other tools that we've rolled out? We really do. We've really appreciated the IQ Case Strategy system and method for allowing us to do our work most efficiently and effectively for our clients. That said, I think with the advent of all the AI technologies and the implementation of those into the Lighthouse suite of tools that we've been talking about, we're really, really excited to leverage those materials and do more with them. In particular, IQ Answers, we're really excited to try out on our next case to really help our teams and allow our teams to leverage the information that we're able to glean most quickly from, again, a large set of documents. That's great. Karl, thank you so much for spending time with us today. We really appreciate your partnership. Absolutely. Thank you, Stacy. As we shared in January, we made IQ Answers available as a risk-free trial. IQ Answers is built on a simple promise: ask anything, get accurate answers. It allows teams to extract insight immediately before committing to a full review. In just the past few weeks, dozens of customers have already applied it to millions of their own documents. To show you how that works in practice, I'd like to bring in Cassie Blum, our Senior Director of AI and Analytics. She'll walk through a brief demonstration. Imagine you are an attorney general suing a pharmaceutical company that manufactures a synthetic opioid spray and you're alleging racketeering. You've received 1.5 million documents in production and your team has three weeks before the next case management conference. Traditional review would've meant that you would spend months and likely millions of dollars to interrogate your data. Let's see what IQ Answers can do in the next several minutes. First off, I'm gonna just ask general questions within my dataset, to get a sense of what was going on with this product, who it was prescribed for, and what was happening with those prescriptions. So I'm typing a question about that generally to see what that returns for me. You'll notice here at the end of the query that you get an overall answer, but you also get topics surfaced for you along with citations that match each of these topics that highlight the particularly important parts of the document that can help lead you to the conclusions that are surfaced for you with IQ Answers. So here we see that this was a spray that was approved for breakthrough cancer pain and that that was the FDA intent of the approval. But what I'm also seeing in these topics that is being surfaced for me is that a lot of off-label prescription for non-cancer pain is apparently going on with this product. And I can tell that from these particular documents that I've clicked on here along with the citations where I'm seeing a hundred plus prescriptions a month that are off-label. Well that's interesting. Now I'm also seeing that the salesforce has been explicitly instructed to prioritize high volume opioid prescription writers, regardless of their specialty and regardless of what's happening with that prescription. And I can click into the documents and see that the volume is what is driving here. And then I also can look and see that there's been awareness internally that the approved indication from the FDA is not actually matching the patient population. So that's very interesting. It seems like we definitely have some use here that it was not intended and I could figure that out in about 30 seconds in IQ Answers; I have the foundation. So if the drug is approved for cancer pain, but the company is deliberately targeting non-cancer patients, that's definitely a gap from the FDA approval. And so let's understand how they're going about making this happen. How are they getting doctors to keep writing these prescriptions? I'm gonna go ahead and ask an additional question of IQ Answers to figure out what they're doing to get this volume of prescriptions written. In particular, I noticed that there was something about speaker engagements that they were doing with doctors. So I'm gonna ask about those speaker engagements. You'll see here that basically the speaker engagement program was a way for them to pay doctors for prescriptions. I can see that they were tracking the ROI of those speaker payments as compared to the amount of prescriptions a doctor was writing. And I can also see from the documents and the citations that if they did not feel that a doctor was writing enough prescriptions, they were no longer part of these speaking events, and thus paid through these speaking events. I also see these speaking events are not really genuine educational activity and it looks like the company was aware of this and was terminating doctors when they were not writing enough prescriptions to satisfy the amount of money that they were giving the doctors. So this all does not look very great for them in terms of how these programs were essentially bribery of these doctors. So, if they're bribing the doctors, that still doesn't answer for me what's happening with the insurance companies. The product costs thousands of dollars a month and insurance companies wouldn't just approve that without any questions. So Vantage clearly needed a way to get these insurers to pay. Let me ask about that. I'm gonna type in a question about their programs to deal with the insurance companies and the way in which they instructed their employees. All right, so now I see in my answer that they had created what was called a Vantage Reimbursement Center that was essentially their way of tracking the approvals from the insurance companies. And it looks like from the topics that's being surfaced here, that they were also using it as a vehicle to instruct their staff on how to deceive the insurance companies. In particular here, you can see in this document where they talk about using particular types of language, lingo, or diagnoses to get the insurance companies to approve the use of these drugs for the written prescriptions. But it also looks like they know that these are false statements that are being made when they are talking with the insurance companies. You can see here they're tracking and they want a 100% authorization success rate. So they're essentially giving all of their employees the way to beat the system and to cheat the insurance company and to get them to approve something under false pretenses. So, I now have two parallel fraud streams that are emerging here. One to bribe doctors to write prescriptions, one to deceive insurance companies to pay. For a RICO case I also need to know that this was coming directly from management and from the top. So let's see if I can find that out in the dataset and ask a question about that to help me win my conviction. Alright, so now I'm gonna ask in particular, what evidence do we have that the CEO knew about this speaker program and this insurance program? Now that I'm asking this of the documents, what I can see here that is surfacing is that in fact the CEO did have direct knowledge and there's multiple documents that substantiate this. And in particular you can see this one here where he personally directs a speaker fee increase that is tied to prescription volume. In this document, you would have him talking about different tiers of speaker programs and that someone is no longer going to be invited to be a part of these speaker programs if they are not writing a certain amount of prescriptions. There's one here that surfaces a document where a hundred percent of the authorization success rate is something that he is directing and he's also calling for in the data. You also see here that I have additional follow-up questions that I can be asked that are being surfaced by IQ Answers that will help me investigate and interrogate each one of these particular questions and the information I found up in these questions. But overall, it looks like I, in a matter of minutes, have been able to find key documents and important fact timelines that help me substantiate my case and know that I have a good chance of success moving forward and make me feel set up really well for my next case management conference. Stacy, I'm gonna send it back over to you. Thank you, Cassie. Given the strong response we've seen, we are extending the IQ Answers risk-free trial. If you'd like to get started, visit iq-answers.com and explore how it can help your team surface insight earlier, narrow scope faster, and make more confident decisions. And if you'll be at Legalweek, we'd welcome the opportunity to connect in person and talk through how LighthouseIQ can support your matters and your priorities. And if not, we're also happy to set up time to meet with you at your convenience. Thank you again for spending time with us today, and we look forward to continuing the conversation.

Join Lighthouse Senior Director of AI & Analytics, Cassie Blum, for a quick demo of IQ Answers inside of LighthouseIQ. Today, I'll show you how LighthouseIQ transforms your entire workflow. For this time together, we'll focus on trial and deposition prep from high level research to comprehensive interview kits. I wish I had time to show you all of LighthouseIQ, but today we're going to focus on IQ Answers and IQ Case Strategy. Let's just dive right on into Lighthouse IQ Answers. I am trying to get an early assessment of what's in my data. Specifically, how did one of my custodians interact with the subject matter at dispute? So I have custodian Jane Hopper that I would like to know what her involvement was with that campaign. So I'm going to type in this question. What was Jane Hopper's involvement with JUUL's youth marketing strategy and appeal to minors? And when we talk about how it analyzes your data, IQ Fabric is doing something proprietary that's a little different from other tools on the market that helps with both its accuracy and its scale. The way in which specifically that we chunk the data for analysis is unique to us in that we make sure with our proprietary chunker, that we are not cutting through sentences or themes. That we are leaving those intact for when we are doing the underlying query. The other thing that it's doing for you is it's surfacing follow up questions for you. So you'll see here I have additional lines of inquiry I can undertake now as a result of what has surfaced in this original question that I queried. So now if I click ask in these, it will run that question for me. And similarly, I now have those results returning to me with document IDs, with highlighting and with citations in the particular record. Now let's switch to IQ Case Strategy. This is the stage of your case where mistakes and miss data cannot happen. It's critical for your frontline litigators to be armed with all the proper data and insights for your matter. So continuing to inquiry Jane Hopper's data set, I'm now inside of the deposition summary for this particular custodian. You'll see here that I not only have a summary, but I have several other important artifacts that are going to help me navigate my trial prep. I have topics that have also been surfaced, and so if I look at these topics, I can click into them and I can see a chronological summary of the key events in this topic. I can also see who is communicating about it. And, if I want to learn even more about those communicants, I can click into our communications widget where I see not only who these folks are and what the particular discourse was, but we see sentiment in here as well. I also have the ability to add a note or a transcript here if I'm collaborating with others in my case or just for my own purposes of preparation. And as with all things in LighthouseIQ, we also have the ability to use AI with natural language query to ask a question of the dataset. It is really a whole ecosystem that will let you interact with your data. And there you'll see, and I've typed in, did Jane Hopper express concern about JUUL's compliance with youth prevention playbook for international marketing? And again, I will see that an answer is returned for me that helps me get additional insight into that from this deposition. This is just a little bit of what you can see in LighthouseIQ. Hopefully you get a flavor of what's possible with IQ Answers and IQ Case Strategy.

Introducing LighthouseIQ: A next-generation intelligence platform and AI application suite designed to provide legal and compliance teams with earlier insights, regulatory defensibility, and significant cost savings.Learn more about LighthouseIQ.Transcript:In every matter, there's a moment that changes everything. The moment information turns into understanding the moment, every decision becomes clearer, faster, the moment you connect with confidence. At Lighthouse, that moment is what we build for.We innovate with purpose to empower legal teams to act with clarity and confidence.We deliver value today while shaping what comes next, and now we take the next step together. Hello and welcome. Thank you for joining us today.I'm Stacy Ibarra, chief Marketing Officer at Lighthouse. We are thrilled to have you join us for the announcement of our battle tested platform that is truly groundbreaking, one that will drive significant new business value for you. A true game changer.Lighthouse has taken advantage of advancements in AI and large language models to provide you with a solution that gives you continuous innovation insight the moment you need it, with no limits to speed or scale, it is secure, trusted, and already regulator approved. Here's what you can expect in today's webcast.First, you'll hear from Lighthouse CEO Ron Markit, Ron Mul unveil how Lighthouse is bringing the power of AI to you in a way that is grounded in real workflows and real results. We'll share a demo that shows how we deliver insight the moment you need it. I'll also speak with two of our clients who are already putting this into practice on complex matters, under real deadlines and real scrutiny.And we'll close by sharing how you can experience this firsthand through an opportunity to trial one of our applications. And with that, I'll turn it over to Lighthouse, CEO, Ron Marcus. Thank you Stacy, and thank you all for joining us.I'm Ron Markezich, CEO of Lighthouse. Before joining Lighthouse three years ago, I spent 27 years at Microsoft.The bulk of my time at Microsoft was helping build and scale Microsoft 365. I joined Lighthouse because I saw a huge opportunity to create more value for the eDiscovery industry. Through both great expertise at Lighthouse and an awesome AI based technology platform that existed.Lighthouse is a pioneer with large language models and ai. Before AI became headlines everywhere. Over the last three years, we've been putting that AI foundation to work, refining it, pressure testing on real matters, and deliberately building towards this moment.Today we're announcing availability of LighthouseIQ. LighthouseIQ is where intelligence meets performance.It's a purpose-built eDiscovery intelligence solution made up of two core layers IQ applications designed to address your specific business needs and IQ fabric as the foundation that weaves everything together. LighthouseIQ is built to accept data from any source system because you always own your data. In LighthouseIQ, we're committed to meeting you exactly where your business needs require.LighthouseIQ is designed to help legal teams understand their data faster, act smarter and save money. LighthouseIQ embeds intelligence and all you do to make new opportunities possible Today, LighthouseIQ includes four applications designed specifically for eDiscovery workflows. These applications work seamlessly together and are already proven on some of the world's largest and most complex matters.These are not chatbots. They're production grade AI powered applications.IQ Answers allows you to ask anything of your data set and receive immediate accurate answers. IQ Case Strategy helps you create sharper strategies and smarter actions because we know inside alone doesn't win cases Action does. IQ Review delivers reliable relevance and high quality classification at scale and speed across your entire data.Corpus IQ Priv generates precise privilege identification and logging given you confidence in your outcomes. This is intelligence that works at the moments you need it. IQ Fabric is the foundational element of LighthouseIQ that weaves everything together into one solution.The power of IQ Fabric comes from three innovative capabilities. Processing standardizes at massive scale preserves fidelity and handles every data format cognition so you can quickly and accurately interpret content.Surface key insights and deepen understanding of your data. Orchestration connects workflows and coordinates tools and teams to power seamless execution. Together these capabilities turn raw data into real actions at any scale and speed your business demands.Enough of me talking. Let's show you. I want to introduce Cassie Blum, a senior director at Lighthouse who has been bringing LighthouseIQ to life for clients.Thanks, Ron. Today I'll show you how LighthouseIQ transforms your entire workflow. For this time together we'll focus on trial and deposition prep from high level research to comprehensive interview kits.I wish I had time to show you all of LighthouseIQ, but today we're gonna focus on IQ Answers and IQ Case Strategy. Let's just dive right on into LighthouseIQ answers. I am trying to get an early assessment of what's in my data.Specifically how did one of my custodians interact with the subject matter at dispute? So I have custodian Jane Hopper that I would like to know what her involvement was with that campaign. So I'm gonna type in this question.What was Jane Hopper's involvement with Jules Youth marketing strategy and appeal to miners? And when we talk about how it analyzes your data, LighthouseIQ Fabric is doing something proprietary that's a little different from other tools on the market that helps with both its accuracy and its scale. The way in which specifically that we chunk the data for analysis is unique to us in that we make sure with our proprietary chunker that we are not cutting through sentences or themes that we are leaving those intact for when we are doing the underlying query.The other thing that it's doing for you is it's surfacing follow up questions for you. So you'll see here I have additional lines of inquiry I can undertake now as a result of what has surfaced in this original question that I queried. So now if I click ask in these, it will run that question for me.And similarly, I now have those results returning to me with document IDs, with highlighting and with citations in the particular record. Now let's switch to IQ Case Strategy. This is the stage of your case where mistakes and miss data cannot happen.It's critical for your frontline litigators to be armed with all the proper data and insights for your matter. So continuing to inquiry Jane Hopper's data set, I'm now inside of the deposition summary for this particular custodian. You'll see here that I not only have a summary, but I have several other important artifacts that are gonna help me navigate my trial prep.I have topics that have also been surfaced, and so if I look at these topics, I can click into them and I can see a chronological summary of the key events in this topic. I can also see who is communicating about it. And if I want to learn even more about those communicants, I can click into our communications widget where I see not only who these folks are and what the particular discourse was, but we see sentiment in here as well.I also have the ability to add a note or a transcript here if I'm collaborating with others in my case or just for my own purposes of preparation. And as with all things in LighthouseIQ, we also have the ability to use AI with natural language query to ask a question of the dataset. It is really a whole ecosystem that will let you interact with your data.And there you'll see, and I've typed in did Jane Hopper express concern about JUULs compliance with Youth Prevention Playbook for international marketing? And again, I will see that an answer is returned for me. That helps me get additional insight into that from this deposition.This is just a little bit of what you can see in LighthouseIQ. Hopefully you get a flavor of what's possible with IQ Answers and IQ K strategy, iq, priv, and IQ Review are incredibly powerful as well. We'll have clients here in a bit that have used all four applications who will share more.We'd also love to follow up to show you the full set of LighthouseIQ capabilities when you have time. But for now, I'll send it back to Ron. Thank you Cassie.I always love seeing how LighthouseIQ can empower our clients. What makes LighthouseIQ different is not just the technology, it's the expertise behind it. We have a seasoned team of eDiscovery professionals.Lighthouse averages under 8% annual attrition, which is excellent for any industry, but that also means our teams remain consistent and committed to your business to serve your needs. That expertise shows up for clients every single day because we know technology alone does not deliver successful outcomes. Lighthouse also remains committed to Relativity as a core part of our platform, relativity is a common review platform for the clients we serve.Our strategy is not to disrupt what already works. It is to enhance it. We will continue our strong partnerships in tight integration with Relativity.Ensuring LighthouseIQ delivers intelligence fully integrated with Relativity. We built LighthouseIQ to support the most complex client needs while also scaling seamlessly to small matters so you can trust one platform for all of your eDiscovery work to earn that trust. LighthouseIQ is modern by design as we keep innovating, incorporating both Lighthouse technology and leading innovation from across the industry.So the platform is always improving and you are not locked in to any one single software provider. It's built for early intelligence so you don't have to wait for documents to move through an entire workflow before gaining insight. You can get intelligence immediately because we serve some of the world's largest and most complicated matters.It was important to ensure LighthouseIQ has unlimited speed and scale. There are absolutely no limits on the number of documents in your matters or meeting your aggressive deadlines. We also know how important it is for LighthouseIQ to be secure, trusted, and accepted by regulators.So we made this solution defensible by design from the beginning. As I mentioned earlier, LighthouseIQ has been battle tested on some of the world's largest and most complex eDiscovery matters. To date, we've analyzed over 1.4 billion documents with LighthouseIQ, including multiple matters exceeding 25 million documents.In fact, we have had a recent matter with 33 million documents analyzed by LighthouseIQ in hours not days. There are no limits to our scale and we will help you meet the most aggressive of your deadlines. And yes, LighthouseIQ has been accepted by the FTC and DOJ, but don't just take it from us.Stacy will come back and speak with a couple of our clients about their experience with LighthouseIQ. Welcome back Stacy. Thanks Ron.We've been fortunate to have industry leaders involved in the development of these products. They've helped guide and shape LighthouseIQ. They've been generous enough to share their time to tell us how they've used LighthouseIQ.I had a chance to sit down with Robert Keeling at Redgrave. He's been in the industry for over 20 years and brings a lot of experience. Hi Robert.I know you're a super busy guy. You've got lots going on at Redgrave. I really appreciate you taking the time to come talk about LighthouseIQ with us today.Oh, thank you. Happy to do so. We have, several matters ongoing with you guys where we're using the LighthouseIQ tool in real time, so I'm excited to talk about it.I know, I think you were coming in earlier with your phone already talking about a matter with one of our, our team members. Yes. I happen to be, on a call with one of your colleagues right before this about about a matter that I'm working with you guys on.so yes, look, looking forward to our discussion. Tell us a little bit about your role at Redgrave. Yes, I'm co-managing partner at Redgrave.and in my practice I work with clients across a range of industries, on their litigations and white collar matters. specifically I partner with Merits counsel and we handle the discovery, both the document review, the privilege review process, and more technical aspects of discovery, for our clients, to try and make the discovery process as efficient and as effective as possible. Great.Congratulations on your new title, by the Way. Thank you. I appreciate it.Why don't you tell me a little bit about your experience using IQ proof? Obviously using the tool to identify privileged communications is really effective and we've had a lot of success. But equally effective is using the tool to help identify what documents are not privilege.And that can really make a review much more efficient, much more effective. For example, we can take documents that they may hit on common privileged generic terms like, like privilege or, or, or legal, but we then look to see, well, what does the Priv IQ tool say about these documents?And if the tool is saying these documents are are not privileged or unlikely to be privileged, we don't review those documents. and instead we'll just have them go out the door without human review, which significantly decreases the overall cost of the radio. Why don't you tell me a little bit about using the tool for priv logging?Yes, we've been working with the Lighthouse team, for IQ for logging now for over two years. and at present, the the technology I would say is rather remarkable. It is, at least as comparable to human review, for creating log entries.I personally think it's superior to human review when that review is set up to have a dropdown or so-called pick list for all options of the prologue in including after the reline. And so what we've done is we worked with the Lighthouse team to have the IQ footprint tool create log entries, for real matters, that have gone out after QC from us, but otherwise without the involvement of a large contract attorney team, which has really replaced a substantial pain point for the discovery process, privilege logging is oftentimes one of the most expensive and tedious parts of discovery. And having a tool like IQ for PRIV replaced that, at least on the human side has, is really I think a significant game changer for us and has led to a lot of efficiencies for our clients.Love to hear your experience with IQ Review and also what difference it made in knowing that this approach has been approved by the FTC and DOJ. I have deep experience with the IQ four review tool. It is the tool of choice when we're working with Lighthouse on second requests, both before the DOJ and the FTC.the FTC and DOJ routinely approve use of IQ four review on second request matters, and we've had good success employing them across very large billion dollar. Second request matters that have been very complex and detailed where we've had to comply on tight timelines. I know you've used IQ Answers in the past.I'd love to hear your experience with that and outcomes that you've been able to achieve. So IQ Answers has been really helpful for us in several matters. For example, we had a matter where we had to respond to very detailed interrogatory requests, and we did, I had very detailed responses.after submitting them though, the receiving party threatened to go to the court on us because they said that we had not cited any documents in our responses. So very quickly we worked with the Lighthouse team and the IQ Answers tool and used the IQ Answers tool, to essentially identify documents relating to each of our interrogatory responses. We basically fed the interrogatory into the tool and our answer, and it provided numerous documents that would support our answer.We then, supplemented our interrogatory responses with the documents identified by IQ Answers and avoided any, court involvement or a motion to compel. So it was a very successful, use of the tool. That's great.It's so great to hear how our clients are using and benefiting from LighthouseIQ. CJ Mahoney, a partner at Cleary Gotlieb has also been using the LighthouseIQ tools. Hi cj.Hey Stacy. It's good to see you. Well, you've had a good year this year with the bills in the playoffs and Miami and the national championship.It's been pretty good. just made partner this year too. But, Miami's probably the most important one of those three things Partner's pretty important to you.So why don't you introduce yourself and tell us a little bit more about your role and the matters you handle at Cleary. Sure. I have been the head of Cleary's eDiscovery Group for a number of years now.I have been at Cleary for about 20 years leading a team of over a hundred attorneys. We handle very large antitrust matters, litigations, other types of government investigations. We are dealing with really large data sets.And so our partnership with Lighthouse is incredibly important to us. Together, we've built a lot of great, tools and especially AI tools to help us, manage a lot of the challenges of these matters. So tell me a little bit of how you've used IQK strategy.Yeah, so, case strategy is really important for us at the start of matters. Prior to, a tool like this, we didn't really have an efficient way to get to key documents early that might make a difference in how we approach the case From a strategic perspective. With case strategy, we now have a much more efficient solution than manual review and boole and search terms to arrive at a really precise, useful population of key documents that helps us understand how to proceed in the case.A big example for me from the past few months is a contract dispute. our client had a dispute with one of their customers regarding cancellation of a contract. Our client felt that they had damages under the contract for the cancellation, but they wanted us to test their theories of damages.They also wanted us to use information from their dataset to help substantiate their damages claimed and arrive at a calculation using, case strategy. We were able to ask a number of prompts, arrive at some really good reporting and a population of around 6,000 documents to, review to substantiate that claim and to arrive at a damages calculation that's backed up by data. We actually benchmarked that against older standard processes and what a review would've looked like without case strategy and found that we would've had to review at least five times the amount of documents.So the savings are huge, but it's not just about the savings. We are actually able to find information that we probably would not have found without search terms. And, today's a huge day for us because just today, this matter settled, thanks to the work we did in case strategy.Our client was able to obtain really favorable monetary settlement. and as importantly, they were able to avoid, the court process in order to get that settlement. What a fantastic outcome.Yeah, it was really great. Would love to hear your experience with IQ Answers and how it's helped you solve big problems. We used IQ Review to arrive at a really precise production set.we were able to achieve nearly 90% precision. so much better than you would expect from traditional human review. But it does leave the issue of, okay, I've got this corpus of responsive documents.I now need to know what's in there as I'm making productions. We were getting pressure from the government regulator to make productions very quickly. So we were trying to identify portions of that responsive set, that could go out without any worry that we are producing concerning documents without fully understanding them.We ran these prompts over potential productions and we're able to identify many documents that were potentially concerning and hold them back from production. While we considered their importance, this was extremely meaningful to the client because although we eventually have to produce those documents, we really want to be able to understand and tell their story to the opposing party at the time of production. Thank you so much for your time, cj, thank you for the partnership.Well, thank you for the partnership. always happy to speak about our great experiences with Lighthouse. Thank you Robert and CJ for sharing your experience with us.As you heard, LighthouseIQ has already been battle tested on some of the largest and most complex matters in the world. The perspectives you just heard bring that proof to life and show what is possible when intelligence is brought forward and paired with experience. To understand the value of LighthouseIQ, we want you to experience it firsthand.The best way to do that is through IQ Answers, which we are making available through a risk-free trial for qualified clients. This allows you to see how quickly you can surface insight, gain clarity, and move forward with confidence on your next matter. There is no risk and immediate value.You can begin uncovering meaningful insights in minutes to get started. Visit IQ Answers.com. Well again, thank you for spending time with us today.We are excited about the benefit you'll receive from LighthouseIQ and we look forward to partnering with you.

Hear what Robert Keeling, Partner at Redgrave, has to say about LighthouseIQ in this interview with Lighthouse CMO, Stacy Ybarra. We have, several matters ongoing with you guys where we're using the LighthouseIQ tool in real time, so I'm excited to talk about it. Yes, I'm co-managing partner at Redgrave in my practice, I work with clients across a range of industries, on their litigations and white collar matters. specifically I partner with merits counsel and we handle the discovery, both the document review, the privilege review process, and more technical aspects of discovery, for our clients, to try and make the discovery process as efficient and as effective as possible. Obviously, using the tool to identify privileged communications is really effective and we've had a lot of success. But equally effective is using the tool to help identify what documents are not privileged. -. And that can really make a review much more efficient, much more effective. IQ Answers has been really helpful for us in several matters. For example, we had a matter where we had to respond to very detailed interrogatory requests, and we did, had very detailed responses. after submitting them though, the receiving party threatened to go to the court on us because they said that we had not cited any documents in our responses. So very quickly we worked with the Lighthouse team and the IQ Answers tool, and used the IQ Answers tool, to essentially identify documents relating to each of our interrogatory responses. We basically fed the interrogatory into the tool and our answer, and it provided numerous documents that would support our answer. We then, supplemented our interrogatory responses with the documents identified by IQ Answers and avoided any, court involvement or a motion to compel. So it was a very successful, use of the tool.

Hear what Christian J Mahoney, Partner at Cleary Gottlieb, has to say about LighthouseIQ in this interview with Lighthouse CMO, Stacy Ybarra. I have been the head of Cleary's eDiscovery Group for a number of years now. I have been at Cleary for about 20 years leading a team of over a hundred attorneys. We handle very large antitrust matters, litigations, other types of government investigations. We are dealing with really large data sets and so our partnership with Lighthouse is incredibly important to us. Together, we've built a lot of great, tools and especially AI tools to help us, manage a lot of the challenges of these matters. Case strategy is really important for us at the start of matters.Prior to, a tool like this, we didn't really have an efficient way to get to key documents early that might make a difference in how we approach the case. From a strategic perspective with case strategy, we now have a much more efficient solution than manual review and boole and search terms to arrive at a really precise, useful population of key documents that helps us understand how to proceed in the case. A big example for me from the past few months is a contract dispute. our client had a dispute with one of their customers regarding cancellation of a contract. Our client felt that they had damages under the contract for the cancellation, but they wanted us to test their theories of damages. They also wanted us to use information from their dataset to help substantiate their damages claimed and arrive at a calculation using, case strategy.We were able to ask a number of prompts, arrive at some really good reporting and a population of around 6, 000 documents to, review to substantiate that claim and to arrive at a damages calculation that's backed up by data. We actually benchmarked that against older standard processes and what a review would've looked like without case strategy and found that we would've had to review at least five times the amount of documents. So the savings are huge, but it's not just about the savings. We are actually able to find information that we probably would not have found without search terms. And, today's a huge day for us because just today, this matter settled. thanks to the work we did in case strategy.Our client was able To obtain really favorable monetary settlement, and as importantly, they were able to avoid, the court process in order to get that settlement. We were getting pressure from the government regulator to make productions very quickly, so we were trying to identify portions of that responsive set, that could go out without any worry that we are producing concerning documents without fully understanding them. We ran these prompts over potential productions and were able to identify many documents that were potentially concerning and hold them back from production. While we considered their importance, this was extremely meaningful to the client because although we eventually have to produce those documents, we really want to be able to understand and tell their story to the opposing party at the time of production.

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