Resources

Collectively, we have sent an average of 306.4 billion emails each day in 2020. Add to that 23 billion text messages and other messaging apps, and you get roughly 41 million messages sent every minute[1]. Not surprisingly, there have been at least one or two articles written about expanding data volumes and the corresponding impact on discovery. I’ve also seen the occasional post discussing how the methods by which we communicate are changing and how “apps that weren’t built with discovery in mind” are now complicating our daily lives. I figured there is room for at least one more big data post. Here I’ll outline some of the specific challenges we’ll continue to face in our “new normal,” all while teasing what I’m sure will be a much more interesting post that gets into the solutions that will address these challenges.Without further delay, here are six challenges we face when working with large data sets and some insights into how we can address these through data re-use, AI, and big data analytics:Sensitive PII / SHI - The combination of expanding data volumes, data sources, and increasing regulation covering the transmission and production of sensitive personally identifiable information (PII) and sensitive health information (SHI) presents several unique challenges. Organizations must be able to quickly respond to Data Subject Access Requests (DSARs), which require that they be able to efficiently locate and identify data sources that contain this information. When responding to regulatory activity or producing in the course of litigation, the redaction of this content is often required. For example, DOJ second requests require the redaction of non-responsive sensitive PII and/or SHI prior to production. For years, we have relied on solutions based on Regular Expressions (RegEx) to identify this content. While useful, these solutions provide somewhat limited accuracy. With improvements in AI and big data analytics come new approaches to identifying sensitive content, both at the source and further downstream during the discovery process. These improvements will establish a foundation for increased accuracy, as well as the potential for proactively identifying sensitive information as opposed to looking for it reactively.Proprietary Information - As our society becomes more technologically enabled, we’re experiencing a proliferation of solutions that impact every part of our life. It seems everything nowadays is collecting data in some fashion with the promise of improving some quality of life aspect. This, combined with the expanding ways in which we communicate means that proprietary information, like source code, may be transmitted in a multitude of ways. Further, proprietary formulas, client contacts, customer lists, and other categories of trade secrets must be closely safeguarded. Just as we have to be vigilant in protecting sensitive personal and health information from inadvertent discloser, organizations need to protect their proprietary information as well. Some of the same techniques we’re going to see leveraged to combat the inadvertent disclosure of sensitive personal and health information can be leveraged to identify source code within document populations and ensure that it is handled and secured appropriately.Privilege - Every discovery effort is first aimed at identifying information relevant to the matter at hand, and second to ensure that no privileged information is inadvertently produced. That is… not new information. As we’ve seen the rise in predictive analytics, and, for those that have adopted it, a substantial rise in efficiency and positive impact on discovery costs, the identification of privileged content has remained largely an effort centered on search terms and manual review. This has started to change in recent years as solutions become available that promise a similar output to TAR-based responsiveness workflows. The challenge with privilege is that the identification process relies more heavily on “who” is communicating than “what” is being communicated. The primary TAR solutions on the market are text-based classification engines that focus on the substantive portion of conversations (i.e. the “what” portion of the above statement). Improvments in big data analytics mean we can evaluate document properties beyond text to ensure the “who” component is weighted appropriately in the predictive engine. This, combined with the potential for data re-use supported through big data solutions, promises to substantially increase our ability to accurately identify privileged, and not privileged, content.Responsiveness - Predictive coding and continuous active learning are going to be major innovations in the electronic discovery industry…would have been a catchy lead-in five years ago. They’re here, they have been here, and adoption continues to increase, yet it’s still not at the point where it should be, in my opinion. TAR-based solutions are amazing for their capacity to streamline review and to materially impact the manual effort required to parse data sets. Traditionally, however, existing solutions leverage a single algorithm that evaluates only the text of documents. Additionally, for the most part, we re-create the wheel on every matter. We create a new classifier, review documents, train the algorithm, rinse, and repeat. Inherent in this process is the requirement that we evaluate a broad data set - so even items that have a slim to no chance of being relevant are included as part of the process. But there’s more we can be doing on that front. Increases in AI and big data capabilities mean that we have access to more tools than we did five years ago. These solutions are foundational for enabling a world in which we continue to leverage learning from previous matters on each new future matter. Because we now have the ability to evaluate a document comprehensively, we can predict with high accuracy populations that should be subject to TAR-based workflows and those that should simply be sampled and set aside.Key Docs - Variations of the following phrase have been uttered time and again by numerous people (most often those paying discovery bills or allocating resources to the cause), “I’m going to spend a huge amount of time and money to parse through millions of documents to find the 10-20 that I need to make my case.” They’re not wrong. The challenge here is that what is deemed “key” or “hot” in one matter for an organization may not be similar to that which falls into the same category on another. Current TAR-based solutions that focus exclusively on text lay the foundation for honing in on key documents across engagements involving similar subject matter. Big data solutions, on the other hand, offer the capacity to learn over time and to develop classifiers, based on more than just text, that can be repurposed at the organizational and, potentially, industry level.Risk - Whether related to sensitive, proprietary, or privileged information, every discovery effort utilizes risk-mitigation strategies in some capacity. This, quite obviously, extends to source data with increasing emphasis on comprehensive records management, data loss prevention, and threat management strategies. Improvements in our ability to accurately identify and classify these categories during discovery can have a positive impact on left-side EDRM functional areas as well. Organizations are not only challenged with identifying this content through the course of discovery, but also in understanding where it resides at the source and ensuring that they have appropriate mechanisms to identify, collect and secure it. Advances in AI and big data analytics will enable more comprehensive discovery programs that leverage the identification of these data types downstream to improve upstream processes.As I alluded to above, these big data challenges can be addressed with the use of AI, analytics, data reuse, and more. Now that I have summarized some of the challenges many of you are already tasked with dealing with on a day-to-day basis, you can learn more about actual solutions to these challenges. Check out my colleague’s write up on how AI and analytics can help you gain a holistic view of your data.To discuss this topic more or to ask questions, feel free to reach out to me at NSchreiner@lighthouseglobal.com.[1] Metrics courtesy of Statistachat-and-collaboration-data; ai-and-analyticsprivilege, analytics, ai-big-data, data-re-use, phi, pii, blog, chat-and-collaboration-data, ai-and-analyticsprivilege; analytics; ai-big-data; data-re-use; phi; pii; blognick schreiner

In our four-part blog series on Schrems II and its impacts, we have already given the state of data transfers in light of the Schrems II decision as well as some practical tips on how to conduct a risk assessment. In sum, the foundation upon which companies have transferred data overseas for the last half-decade was recently shaken. Companies are left with no good legal options for data transfer so, instead, they need to make calculated risk assessments based on business need and convenience versus compliance with an unknown and quickly changing legal landscape.For those companies who have chosen Microsoft as their cloud provider, Microsoft has taken additional steps to alleviate some of the risks. In addition, there are some specific supplementary measures companies can take in their Microsoft 365 (M365) environment to mitigate some risk. In this third part of our series, we will consider the position if you are analysing data transfers that take place using M365, Microsoft’s flagship software-as-a-service tool, which is in use by many entities operating within Europe.It is worth pointing out that Microsoft has responded quickly to the upheaval. The EDPB issued its supplementary measures on November 11th, 2020, and by November 19th, Microsoft issued a press release entitled “New Steps to Defend Your Data.” Microsoft explained it was strengthening the rights of its public sector and enterprise customers in relation to data by including an Additional Safeguards Addendum into standard contractual terms. That addendum would give contractual force to the new steps Microsoft laid out in terms of defending customers’ data, namely that Microsoft:will challenge every government request for public sector or enterprise data from any government where there is a lawful basis for doing so; andwill compensate a public-sector or enterprise-customer user if data is disclosed in response to a government request in violation of the GDPR.Microsoft pointed out that these commitments exceeded the EDPB’s recommendations (presumably referring to the contractual supplementary measures in the EDPB guidance). These changes have received a mixed response, but it is interesting to see that the data protection authorities within three of the German states (Baden -Württemberg, Bavaria, and Hesse) issued a joint opinion that this was a move in the right direction since it included significant improvements for the rights of European citizens and was a clear signal to other providers to follow suit.So at a macro level, Microsoft has taken very public steps. However, that does not remove the need to carry out the analysis set out by the EDPB or, in general, carry out a risk assessment to give you a thorough understanding of any risks associated with using M365. Here are some specific considerations to keep in mind:As to the first step of the EDPB recommendations, identifying your data transfers, it is our understanding that Microsoft will shortly be publishing more detailed data maps which will help.The Microsoft white paper on the necessary elements for monitoring, securing, and assessing cloud storage is a very helpful resource. An updated version of this is also expected shortly.As part of your assessment, you should review the Microsoft Online Services Data Protection Addendum, in particular, the Data Transfers and Location sections, and the amended terms arising from Microsoft’s recent press release.When carrying out your risk assessment or transfer impact assessment, you should consider carefully the extent to which M365 can be configured to reduce the amount of personal data leaving Europe. More specifically, there are six areas upon which you could focus: Multi-geo: With multi-geo, a company operating in Europe can choose to have its Exchange Online (i.e., email), its SharePoint Online, and its OneDrive for Business data stored, at rest, within Europe. Multi-geo reduces the amount of data that would be transferred to the US in comparison to having the geo (Microsoft’s word for the central hub where data is stored) within the US. This is probably the most significant step a company can take to reduce data transfers. Choosing whether or not to enable applications: Certain applications such as Sway, Microsoft’s newsletter application, will have their data stored in the US irrespective of whether a company chooses to have a multi-geo setup. A company might weigh the pros and cons of each application, which involves data being stored in the US, and decide that it could operate without that application.Configuration settings at an application level: There are many settings within M365 at an application level that will vary the amount of data being generated and processed. Assessing each application in turn and deciding the specific configuration within that application can make a significant difference to the amount of personal data being created, moved, or stored. For more details on how to evaluate this for the popular collaboration tool, Teams, you can review this write-up.Encryption: Explore encryption thoroughly and look to implement it, if practical, as an additional technical safeguard. There a number of good resources explaining how encryption operates and the options available to add additional encryption. Here is a good starting point for learning about Microsoft’s encryption options.Customer lockbox: If you configure M365 so that the number of data transfers is reduced to the bare minimum, one area where transfers might still be needed is when there is a need for remote access by Microsoft engineers to provide support. Customer lockbox allows you to give final and limited approval for such access, which you can do after carrying out a specific risk assessment.Audit logs: All significant events in M365 are audited so you should put in place a review of audit logs to support any risk assessments that you complete.It is also more than just good practice to put in place a retention policy within M365, it is essential to ensure that personal data is not being retained for longer than is necessary. Reducing the amount of personal data within an organisation reduces the risk of data breaches that could result in problems under the provisions of the GDPR. Microsoft is following the legal landscape closely so expect to see quick responses from them as things change. But what kinds of changes should companies expect and when? Read the final part of this blog series on what the future may hold.To discuss this topic further, please feel free to reach out to us at info@lighthouseglobal.com.data-privacy; microsoft-365; information-governancemicrosoft, cloud, data-privacy, blog, corporate-legal-ops, data-privacy, microsoft-365, information-governance,microsoft; cloud; data-privacy; blog; corporate-legal-opslighthouse

On July 15, 2022, the mandatory Disclosure Pilot Scheme (PD51U) was officially approved and will operate on a permanent basis within the Business and Property Courts (BP&C) of England and Wales. Originally implemented in 2019 on a temporary pilot basis, it was extended twice and had been set to expire in December of 2022. Its approval means that on October 1, 2022, the pilot will end, and the scheme will officially be known as Practice Direction (PD) 57AD “Disclosure in the Business and Property Courts.”This approval is no surprise to those familiar with the modern disclosure process in the UK. PD51U was originally implemented to address the key issues associated with standard disclosure under Civil Procedure Rule (CPR) 31, such as unwieldly costs and the insurmountable scale of disclosure due to ever-growing corporate data volumes. As per UTB LLC v Sheffield United, the pilot was meant to effect a “culture change” in the reasonableness and proportionality of disclosure requests by streamlining the process in a variety of ways. One of the most notable is through the encouragement of leveraging technology (such as technology assisted review or TAR) and data analytics for document review—even going so far as to mandate the use of TAR in cases where the document count exceeds 50,000.Over the last two years, this push toward implementing more technology to streamline the disclosure process has proven to be a wise one. With a worldwide shift to cloud-based infrastructures and remote working, corporate data volumes have exploded and will only continue to grow. Therefore, the traditional means of disclosure review, wherein a team of reviewers looks at each electronic document one-by-one, is quickly becoming untenable. Utilising technology to streamline review is more imperative than ever and will only grow in importance as data volumes continue to balloon. What 57AD does not mean, however, is that solicitors faced with disclosure need to be data science or technology experts. It simply means that it will become increasingly important for solicitors who are not comfortable with disclosure technology to find a solid managed review partner that can help streamline the disclosure process with technology and meet Practice Direction 57AD requirements. Below are key attributes to look for when seeking such a partner.Look for a managed review partner with expertise on the Disclosure Review Document (DRD)The DRD is meant to facilitate an agreement between parties about what constitutes proportional disclosure, and how to achieve that goal in a cost-effective manner. To do so, it requires parties to identify the key issues of the case and then detail the method of disclosure for each issue, with five methods from which to choose.[1] Each method can have severe impacts on the cost of a matter, as well as the overall outcome of the case for clients. It is vital that someone with in-depth disclosure expertise is involved in the negotiation and completion of this document. Some managed review vendors may be able to provide staffing and project management when it comes to disclosure document review but will not have experts available and capable to provide advice on effective disclosure strategy, including DRD assistance. Without this expertise, a party may find itself agreeing to disclosure methods that significantly balloon budgets or even worse, result in harmful outcomes for clients. Look for a managed review partner who has developed strong defensible workflowsOne of the hallmarks of and impetuses for PD 51U (soon to be PD 57AD) was to streamline the disclosure process in the face of ever-growing and unprecedented data volumes. Understanding when and how to leverage technology to cull and prioritise data for review, as well as how to leverage TAR, is imperative. However, the technology and workflows can seem overwhelming, especially to those who don’t perform disclosure often. Thus, it is essential to find a managed review partner who has access to the best review technology and knows how to leverage that technology to achieve the best results in every type of matter. It is also important that that managed review partner has developed strong defensible workflows for data reduction that can be customised to meet the individual needs of each client.Look for a managed review partner who thinks outside of the traditional linear review approachWhile it may seem simpler to fall back on traditional approaches to the disclosure document review process (i.e., hiring many reviewers to read and categorize each document), it is important to remember that PD 57AD was enacted because that approach is quickly becoming too burdensome for parties. The traditional approach also opens parties up to risk, when reviewers cannot effectively review the volume of documents within the time frames required for disclosure. Today’s larger data volumes and more complicated data increase the risk that human reviewers will miss important documents that were required to be disclosed, or conversely, that they will disclose harmful or sensitive documents that should not have been disclosed. Forward-thinking managed review partners have anticipated this change and have invested in technology and human expertise that can defensibly minimise document volumes so that a discrete number of subject matter experts can look at prioritised categories of pertinent documents, maximizing the value of human review. In this way, a managed reviewer partner can help solicitors move away from an outdated approach to review, while streamlining the disclosure process, keeping litigation budgets in check, minimising risk, and achieving better outcomes. Look for a partner who will help prepare bespoke briefing documentation, right from the outsetWhen a matter needs to scale up quickly and on short notice, the painstaking process of adding new reviewers can explode budgets—not only because of the additional overhead, but also because of the churn and inefficiency created by inconsistent work product from inexperienced, new reviewers. A good managed review partner will prepare for and minimise this churn from the outset, by creating customised briefing documentation that enables new reviewers to roll onto matters seamlessly, without a heavy lift from the client or review manager. Documentation like term glossaries for niche cases (for example, medical inquiries) that are kept in a central repository will help case teams quickly scale up and onboard new reviewers at short notice, while minimizing the churn and risk often thought of as inevitable when adding new reviewers. Look for a partner who has developed ways to ensure quality work from review teamsInconsistent or incorrect decisions from review teams creates additional work, which can decimate budgets. Even when data volumes are culled to more manageable levels, inaccurate review work product can still open clients up to risk, especially when sensitive data is involved. Look for managed review partners who have systems in place to ensure the accuracy of the review team from the outset. For example, some managed review providers will rigorously “test” the work product of review teams, directly after training has finished. This testing process can ensure that each reviewer assigned to the team understands the subject matter and review process, and that from the start of the matter their work product aligns with the case team’s direction. This type of quality control, started at the reviewer selection process, can greatly reduce risk while keeping budgets under control. Look for a managed review partner who ensures value for money in terms of candidatesIn a traditional approach, first pass review for relevance, privilege, and issues are undertaken by UK-based paralegals, with proven experience in reviewing and redacting documents together with a law degree, LPC/GDL, or NALP certification. However, these reviewers can be expensive, and billed at exorbitant hourly rates. Forward-thinking managed review partners often have partnerships with reviewers who have been admitted to Bars outside of the UK, providing an added layer of experience offered at a reduced cost. This complies with the overall message of PD 57AD, in that it offers a reliable basis for costs which promotes the cost-effective and efficient conduct of disclosure. [1] Model A – No order for disclosure; Model B – Limited disclosure; Model C – Request-led, search-based disclosure; Model D – Narrow search-based disclosure (with or without narrative documents); Model E – Wide search-based disclosureediscovery-reviewreview, blog, ediscovery-review,review; blogjennifer cowman

A recent conversation with a colleague in Lighthouse’s Focus Discovery team resonated with me – we got to chatting about TAR protocols and the evolution of TAR, analytics, and AI. It was only five years ago that people were skeptical of TAR technology and all the discussions revolved around understanding TAR and AI technology. That has shifted to needing to understand how to evaluate the process of your team or of opposing counsel’s production. Although an understanding of TAR technology can help in said task, it does not give you enough to evaluate items like the parity of types of sample documents, the impact of using production data versus one’s own data, and the type of seed documents. That discussion prompted me to grab one of our experts, Tobin Dietrich, to discuss the cliff notes of how one should evaluate a TAR protocol. It is not totally uncommon for lawyers to receive a technology assisted review methodology from producing counsel – especially in government matters but also in civil matters. In the vein of the typical law school course, this blog will teach you how to issue spot if one of those methodologies comes across your desk. Once you’ve spotted the issues, bringing in the experts is the right next step.Issue 1: Clear explanation of technology and process. If the party cannot name the TAR tool or algorithm they used, that is a sign there is an issue. Similarly, if they cannot clearly describe their analytics or AI process, this is a sign they do not understand what they did. Given that the technology was trained by this process, this lack of understanding is an indicator that the output may be flawed.Issue 2: Document selection – how and why. In the early days of TAR, training documents were selected fairly randomly. We have evolved to a place now where people are being choosy about what documents they use for training. This is generally a positive thing but does require you to think about what may be over or under represented in the opposing party’s choice of documents. More specifically, this comes up in 3 ways:Number of documents used for training. A TAR system needs to understand what responsive and non-responsive looks like so it needs to see many examples in each category to approach certainty on its categorization. When using too small a sample, e.g. 100 or 200 documents, this risks causing the TAR system to incorrectly categorize. Although a system can technically build a predictive model from a single document, it will only effectively locate documents that are very similar to the starting document. The reality of a typical document corpus is that it is not so uniform as to rely upon the single document predictive model.Types of seed documents. It is important to use a variety of documents in the training. The goal is to have the inputs represent the conceptual variety in the broader document corpus. Using another party’s production documents, for example, can be very misleading for the system as the vocabulary used by other parties is different, the people are different, and the concepts discussed are very different. This can then lead to incorrect categorization of documents. Production data, specifically, can also add confusion with the presence of Bates or confidentiality stamps. If the types of seed documents/training documents used do not mirror typical types of documents expected from the document corpus, you should be suspicious.Parity of seed document samples. Although you do not need anything approaching the perfect parity of responsive and non-responsive documents, it can be challenging to use 10x the number of non-responsive versus responsive documents. This kind of disparity can distort the TAR model. It can also exacerbate either of the above issues, number, or type of seed documents.Issue 3: How is performance measured? People throw around common TAR metrics like recall and precision without clarifying what they are referring to. You should always be able to tell what population of documents these statistics relate to. Also, don’t skip over precision. People often throw out recall as sufficient, but precision can provide important insight into the quality of model training as well.By starting with these three areas, you should be able to flag some of the more common issues in TAR processes and either avoid them or ask for them to be remedied. ai-and-analytics; ediscovery-reviewanalytics, ai-big-data, tar-predictive-coding, blog, ai-and-analytics, ediscovery-reviewanalytics; ai-big-data; tar-predictive-coding; bloglighthouse

Cut-off scores, seed sets, training rounds, confidence levels – to the inexperienced, technology assisted review (TAR) can sound like a foreign language and can seem just as daunting. Even for those legal professionals who have had experience utilizing the traditional TAR 1.0 model, the process may seem too rigid to be useful for anything other than dealing with large data volumes with pressing deadlines (such as HSR Second Requests). However, TAR 2.0 models are not limited by the inflexible workflow imposed by the traditional model and require less upfront time investment to realize substantial benefits. In fact, TAR 2.0 workflows can be extremely flexible and helpful for myriad smaller matters and non-traditional projects, including everything from an initial case assessment and key document review to internal investigations and compliance reviews.A Brief History of TARTo understand the various ways that TAR 2.0 can be leveraged, it will be helpful to understand the evolution of the TAR model, including typical objections and drawbacks. Frequently referred to as predictive coding, TAR 1.0 was the first iteration of these processes. It follows a more structured workflow and is what many people think of when they think of TAR. First, a small team of subject-matter experts must train the system by reviewing control and training sets, wherein they tag documents based on their experience with and knowledge of the matter. The control set provides an initial overall estimated richness metric and establishes the baseline against which the iterative training rounds are measured. Through the training rounds, the machine develops the classification model. Once the model reaches stability, scores are applied to all the documents based on the likelihood of being relevant, with higher scores indicating a higher likelihood of relevance. Using statistical measures, a cutoff point or score is determined and validated, above which the desired measure of relevant documents will be included. The remaining documents below that score are deemed not relevant and will not require any additional review.Although the TAR 1.0 process can ultimately result in a large reduction in the number of documents requiring review, some elements of the workflow can be substantial drawbacks for certain projects. The classification model is most effectively developed from accurate and consistent coding decisions throughout the training rounds, so the team of subject-matter experts conducting the review are typically experienced attorneys who know the case well. These attorneys will likely have to review and code at least a few thousand documents, which can be expensive and time consuming. This training must also be completed before other portions of the document review, such as privilege or issue coding, can begin. Furthermore, if more documents are added to the review set after the model reaches stability (think, a refresh collection or late identified custodian) the team will need to resume the training rounds to bring the model back to stability for these newly introduced documents. For these reasons, the traditional TAR 1.0 model is somewhat inflexible and suited best for matters where the data is available upfront and not expected to change over time (i.e. no rolling collections) so that the large number of documents being excised from the more costly document review portion of the project will offset the upfront effort expended training the model.TAR 2.0, also referred to as continuous active learning (CAL), is a newer workflow (although it has been around for a number of years now) that provides more flexibility in its processes. Using CAL, the machine also learns as the documents are being reviewed, however, the initial classification model can be built with just a handful of coded documents. This means the review can begin as soon as any data is loaded into the database, and can be done by a traditional document review team right from the outset (i.e. there is no highly specialized “training” period). As the documents are reviewed, the classification model is continuously updated as are the scores assigned to each document. Documents can be added to the dataset on a rolling basis without having to restart any portion of the project. The new documents are simply incorporated into the developing model. These differences make TAR 2.0 well suited for a wider variety of cases and workflows than the traditional TAR 1.0 model.TAR 2.0 Workflow ExamplesOne of the most common TAR 2.0 workflows is a “prioritization review,” wherein the highest scoring documents are pushed to the front of the review. As the documents are reviewed the model is updated and the documents are rescored. This continuous loop allows for the most up-to-date model to identify what documents should be reviewed next, making for an efficient review process, with several benefits. The team will review the most likely relevant, and perhaps important, documents first. This can be especially helpful when there are short timeframes within which to begin producing documents. While all documents can certainly be reviewed, this workflow also provides the means to establish a cutoff point (similar to TAR 1.0) where no further review is necessary. In many cases, when the review reaches a point where few relevant documents are found, especially in comparison to the number of documents being reviewed, this point of diminishing returns signals the opportunity to cease further review. The prioritization review can also be very effective with incoming productions, allowing the system to identify the most relevant or useful documents.An alternative TAR 2.0 workflow is the “coverage” or “diverse” review model. In this model, rather than reviewing the highest scoring documents first, the review team focuses on the middle-scoring range documents. The point of a diverse review model is to focus on what the machine doesn’t know yet. Reviewing the middle range of documents further trains the system. In this way, a coverage TAR 2.0 review model provides the team with a wide variety of documents within the dataset. When using this workflow for reviews for productions, the goal is to end up with the documents separated between those likely relevant and those likely not relevant. This workflow is similar to the TAR 1.0 workflow as the desired outcome is to identify the relevant document set as quickly or directly as possible without reviewing all of the documents. To illustrate, a model will typically begin with a bell-shaped curve of the distribution of documents across the scoring spectrum. This workflow seeks to end with two distinct sets, where one is the relevant set and the other is the non-relevant set.These workflows can be extremely useful for initial case assessments, compliance reviews, and internal investigations, where the end goal of the review is not to quickly find and produce every relevant document. Rather, the review in these types of cases is focused on gathering as much relevant information as possible or finding a story within the dataset. Thus, these types of reviews are generally more fluid and can change significantly as the review team finds more information within the data. New information found by the review team may lead to more data collections or a change in custodians, which can significantly change the dataset over time (something TAR 2.0 can handle but TAR 1.0 cannot). And because the machine provides updated scoring as the team investigates and codes more documents, it can even provide the team with new investigational avenues and leads. A TAR 2.0 workflow works well because it gives the review team the freedom to investigate and gain knowledge about a wide variety of issues within the documents, while still ultimately resulting in data reduction.ConclusionThe above workflow examples illustrate that TAR does not have to be the rigid, complicated, and daunting workflow feared by many. Rather, TAR can be a highly adaptable and simple way to gain efficiency, improve end results, and certainly to reduce the volume of documents reviewed across a variety of use cases.It is my hope that I have at least piqued your interest in the TAR 2.0 workflow enough that you’ll think about how it might be beneficial to you when the next document review project lands on your desk.If you’re interested in discussing the topic further, please freely reach out to me at DBruno@lighthouseglobal.com.ai-and-analytics; ediscovery-reviewtar-predictive-coding, blog, ai-and-analytics, ediscovery-reviewtar-predictive-coding; blogdavid bruno

Given the proliferation of data and evolving variety of data sources, in-house counsel teams are beginning to exhaust resources managing increasingly complex case data. self-service, spectra eDiscovery legal technology offers a compelling solution. Consider the impact of inefficiencies faced by in-house counsel, today - from waiting for vendors to load data or provide platform access, to scrambling, to keeping up with advancing technologies, and managing data security risks - it’s a lot. The average in-house counsel team isn’t just dealing with these inefficiencies on large litigations, they’re encountering these issues in even the smallest compliance and internal investigations matters.self-service, spectra solutions offer an opportunity to streamline eDiscovery programs, allowing in-house legal teams to get back to the business of case management and legal counseling. It’s understandable we’re witnessing more and more companies moving to this model.So, once your organization has decided it is ready to step into the future and take advantage of the benefits self-service, spectra eDiscovery solutions have to offer, what’s next? Below, I’ve outlined three best practices for implementing a self-service, spectra eDiscovery solution within your organization. While any organizational change can seem daunting at the outset, keeping the below tips in mind will help your company seamlessly move to a self-service, spectra model.1. Define how you leverage your self-service, spectra eDiscovery solution to scale with ease.One of the key benefits of a quality self-service, spectra solution is that it puts your organization back in the eDiscovery driver’s seat. You decide what cases you will handle internally, with the advantage of having access to an array of eDiscovery expertise and matter management services when needed, even if that need arises in the middle of an ongoing matter. Cloud-based self-service, spectra solutions can readily handle any amount of data, and a quality self-service, spectra solution provider will be able to seamlessly scale up from self-service, spectra to full-service without any interruption to case teams.Having a plan in place regarding how and when you will leverage each of these benefits (i.e. self-service, spectra vs. full-service) will help you manage internal resources and implement a pricing model that fits your organization’s needs.2. Select a pricing model that works for your organization.Every organization’s eDiscovery business is different and self-service, spectra pricing models should reflect that. After determining how your organization will ideally leverage a self-service, spectra platform, decide what pricing model works best for that type of utilization. self-service, spectra solution providers should be able to provide a variety of licensing options to choose from, from an a la cart approach to subscription and transaction models.Prior to communicating with your potential solution provider, define how you plan to leverage a self-service, spectra solution to meet your needs. Then you can consider the type of support you require to balance your caseload with team resources and prepare to talk to providers about whether they can accommodate that pricing. Once you have on-boarded a self-service, spectra solution, be sure to continue to evaluate your pricing model, as the way you use the solution may change over time.3. Discuss moving to a self-service, spectra model with your IT and data security teams .Another benefit of moving to a self-service, spectra model is eliminating the burden of application and infrastructure management. Your in-house teams will be able to move from maintaining (and paying for) a myriad of eDiscovery technologies to a single platform providing all of the capabilities you need without the IT overhead. In effect, moving to a self-service, spectra solution gives your team access to industry-leading eDiscovery technology while removing the cost and hassle of licensing and infrastructure upkeep.A self-service, spectra model also allows you to transfer some of your organization’s data security risk to a solution provider. You gain peace of mind knowing your eDiscovery data and the supporting tech is administered by a dedicated IT and security team in a state-of-the-art IT environment with best-in-class security certifications.Finally, to ensure your organization can realize the full benefit of moving to a self-service, spectra solution, it’s imperative that your IT team has a seat at the table when selecting a solution platform. They can help to ensure that whatever service is selected can be fully and seamlessly integrated into your organization’s systems. Keeping these tips in mind as your organization begins its self-service, spectra journey will help you realize the benefits that a quality self-service, spectra eDiscovery platform can provide. For more in-depth guidance on migrating to self-service, spectra platforms, Brooks Thompson’s blog posts discussing tips for overcoming self-service, spectra objections and building a self-service, spectra business case.ediscovery-review; ai-and-analyticsself-service, spectra, blog, ediscovery-review, ai-and-analyticsself-service, spectra; bloglighthouse

self-service, spectra as a topic has grown significantly in the recent past. With data proliferating at astronomical amounts year over year it makes sense that corporations and firms are wanting increasing control over this process and its cost. Utilizing a self-service, spectra eDiscovery tool is helpful if you want control over your queue as well as your hosted footprint. It is beneficial if your team has an interest and the capability of doing your own ECA. Additionally, self-service, spectra options are useful as they provide insight into specific reporting that you may or may not be currently receiving.Initially, the self-service, spectra model was introduced to serve part of the market that didn’t require such robust, traditional full eDiscovery services for every matter. Tech-savvy corporations and firms with smaller matters were delighted to have the option to do the work themselves. Over time there have been multiple instances in which a small matter scales unexpectedly and must be dealt with quickly, in an all hands on deck approach, to meet the necessary deadlines. In these instances, it’s beneficial to have the ability to utilize a full-service team. When these situations arise it’s critical to have clean handoffs and ensure a database will transfer well.Moreover, we have seen major strides in the self-service, spectra space regarding the capabilities of data size thresholds. self-service, spectra options can now handle multiple terabytes, so it’s not just a “small matter” solution anymore. This gives internal teams incredible leverage and accessibility not previously experienced.self-service, spectra considerations and recommendationsIt’s important to understand the instances in which a company should utilize a self-service, spectra model or solution. Thus, I recommend laying out a protocol. Put a process in place ahead of time so that the next small internal investigation that gets too large too quickly has an action plan that gets to the best solution fast. Before doing this, it’s important to understand your team’s capabilities. How many people are on your team? What are their roles? Where are their strengths? What is their collective bandwidth? Are you staffed for 24/7 support or second requests or are you not?Next, it’s time to evaluate what part of the process is most beneficial to outsource. Who do you call for any eDiscovery related need? Do you have a current service provider? If so, are they doing a good job? Are they giving you a one-size-fits-all solution (small or large), or are they meeting you where you are and acting as a true partner? Are they going the extra mile to customize that process for you? It’s important to continually audit service providers.Think back to past examples. How prepared has your team and/or service provider been in various scenarios? For instance, if an investigation is turning into a government investigation, do you want your team pushing the buttons and becoming an expert witness, or do you have a neutral third party to hand that responsibility off to?After the evaluation portion, it’s time to memorialize the process through a playbook, so that everyone has clear guidelines regardless of which litigator or paralegal internally is working on the case. What could sometimes be a complicated situation can be broken down into simple rules. If you have a current protocol or playbook, ensure your team understands it. Outline various circumstances when the team would utilize self service or full service, so everyone is on the same page.For more on this topic, check out the interview on the Law & Candor podcast on scaling your eDiscovery program from self service to full service. ediscovery-reviewcloud, self-service, spectra, cloud-services, blog, ediscovery-review,cloud; self-service, spectra; cloud-services; bloglighthouse

Legal and IT teams do not necessarily sit at the same lunch table (to use an over-simplified high-school analogy), however, organizations can quickly run into challenges when these teams are not aligned. As corporate data volume and types continue to grow at record speed, it is critical to maintain a technology infrastructure that is not only secure, but also satisfies the legal requirements for managing information. I recently had the privilege of chatting with Craig Shaver, the eDiscovery Program Director at Hilton Worldwide, about the challenges of this electronic data mosaic and innovative strategies to enable collaboration between these groups on the Law and Candor podcast. In this blog, I will review the key challenges we discussed as well as summarize three key solutions to overcoming them in the hopes it will help align your IT and legal teams.To level set, both teams have different priorities. Legal is generally focused on ensuring that the company’s data is protected and retention policies are upheld, while IT is looking for new ways to manage the ever-increasing volume of data to drive efficiency while maintaining budgets. So, when IT moves forward with new technology solutions, large data migrations, moves to the Cloud, or even simple contractual agreements and is not in sync with Legal due to other priorities or lack of communication, items may be missed and can create large downstream issues such as potentially responsive documents going uncollected, being slapped with spoliation charges, or costly and time-consuming rework.Nobody wants unforeseen charges or to loose time and money, so let’s look at some solutions to overcoming these challenges by ensuring collaboration between these two teams. Begin by:Establishing Legal Processes and Policies – Legal needs to first ensure they have effective legal hold processes in place, clear and consistent policies on data retention, as well as defensible deletion policies. Without these in place there is no formal process.Ensuring Participation on Both Sides – It is important to identify and designate a legal and IT liaison to sit on various steering committees and be a part of any technology decisions, migration projects, etc. In some larger, global organizations, you may want at least two or three people from each group involved to attend these meetings, as it can be a lot of work and require travel. Legal will understand the impact on the overall eDiscovery process and can review service-level agreements and SOWs as well.Continuing the Ongoing Partnership and Communication – Post project, it is important to continue to meet regularly (weekly or monthly) with key stakeholders to continue to communicate around upcoming migrations, technology changes, etc., as well as build trust and a further develop relationships. Legal can help IT enforce their deployment and security policies with other departments within the company as well as ensure GDPR compliance and other factors are considered when looking at new products.Enacting these three solutions will help you ensure your teams stay in sync. When legal and IT sit at the same lunch table and stay in communication, organizations are more likely to experience seamless or near-seamless integration of processes, better understand project timelines, reduce friction between very busy teams, maintain a shared understanding each other’s workloads and processes, as well as gain trust amongst the teams, which helps with future projects and getting folks to support one another.To discuss this topic more, reach out to me at bmariano@lighthouseglobal.com.legal-operations; information-governance; data-privacygdpr, ediscovery-process, blog, legal-operations, information-governance, data-privacy,gdpr; ediscovery-process; blogbill mariano

In this two-part series, I interview Gordon J. Calhoun, Esq. of Lewis Brisbois Bisgaard & Smith LLP about his thoughts on the state of eDiscovery within law firms today, including lessons learned and best practices to help attorneys overcome their trepidation of electronic discovery and build a better litigation practice. This first blog focuses on the history of eDiscovery and the logical reasons that attorneys may still try to avoid it, often to the detriment of their clients and their overall practice. IntroductionThe term “eDiscovery” (i.e., electronic discovery) was coined circa 2000 and received significant consideration by The Sedona Conference and others, well in advance of November 2006. That’s when the U.S. Supreme Court amended the Federal Rules of Civil Procedure to include electronically stored information (ESI), which was widely recognized as categorically different from data printed on paper. The amendments specifically mandated that electronic communications (like email and chat) would have been preserved in anticipation of litigation and produced when relevant. In doing so, it codified concepts explored by Judge Shira Scheindlin’s groundbreaking Zubulake v. UBS Warburg decisions.By 2012, the exploding volumes of data led technologists assisting attorneys to employ various forms of artificial intelligence (AI) to allow analysis of data to be accomplished in blocks of time that were still affordable to litigants. The use of predictive coding and other forms of technology-assisted review (TAR) of ESI became recognized in U.S. courts. By 2013 updates to the American Bar Association (ABA) Model Rules of Professional Conduct officially required attorneys to stay current on “the benefits and risks” of developing technologies. By 2015, the FRCP was amended again to help limit eDiscovery scope to what is relevant to the claims and defenses asserted by the parties and “proportional to the needs of the case,” as well as to normalize judicial treatments of spoliation and related sanctions associated with ESI evidence. In the same year, California issued a formal ethics opinion obligating attorneys practicing in California to stay current with ever changing eDiscovery technologies and workflows in order to comply with their ethical obligation of competently providing legal services.In the 15 years that have passed since those first FRCP amendments designed to deal with the unique characteristics of ESI, we’ve seen revolutionary changes in the way people communicate electronically within organizations, as well as explosive growth in the volume and variety of data types as we have entered the era of Big Data. From the rise of email, social media, and chat as dominant forms of interpersonal communication, to organizations moving their data to the Cloud, to an explosion of ever-changing new data sources (smart devices, iPhones, collaboration tools, etc.) – the volume and variety of which makes understanding eDiscovery’s role in litigation more important than ever.And yet, despite more than 20 years of exposure, the challenges of eDiscovery (including managing new data forms, understanding eDiscovery technology, and adhering to federal and state eDiscovery standards) continue to generate angst for most practitioners.So why, in 2021, are smart, sophisticated lawyers still uncomfortable addressing eDiscovery demands and responding to them? To find out, I went to one of the leading experts in eDiscovery today, Gordon J. Calhoun, Esq. of Lewis Brisbois Bisgaard & Smith LLP. Mr. Calhoun has over 40 years of experience in litigation and counseling, and he currently serves as Chair of the firm’s Electronic Discovery, Information Management & Compliance Practice. Over the years he has found creative solutions to eDiscovery challenges, like having a court enter a case management order requiring all 42 parties in a complex construction defect case to use a single technology provider, which dropped the technology costs to less than 2.5% of what they would have been had each party employed its own vendor. In another case (which did not involve privileged communications), he was able to use predictive coding to rank 600,000 documents and place them into tranches from which samples were drawn to determine which tranches could be produced without further review. It was ultimately determined that about 35,000 documents would not have to be reviewed after having put eyes on fewer than 10,000 of the original 600,000.I sat down with Mr. Calhoun to discuss his practice, his views of the legal and eDiscovery industries, and to try to get to the bottom of how attorneys can master the challenges posed by eDiscovery without having to devote the time needed to become an expert in the field.Let’s get right down to it. With all the helpful eDiscovery technology that has evolved in the market over the last 10 years, why do you think eDiscovery still poses such a challenge for attorneys today? Well, right off the bat, I think you’re missing the mark a bit by focusing your inquiry solely around eDiscovery technology. The issue for many attorneys facing an eDiscovery challenge today is not “what is the best eDiscovery technology?” – because many attorneys don’t believe any eDiscovery technology is the best “solution.” Many believe it is the problem. No technology, regardless of its efficacy, can provides value if it is not used. The issue is more fundamental. It’s not about the technology, it is about the fear of the technology, the fear of not being able to use it as effectively as competitors, and the fear of incurring unnecessary costs while blowing budgets and alienating clients.Practitioners fear eDiscovery will become a time and money drain, and attorneys fear that those issues can ultimately cost them clients. Technology may, in fact, be able to solve many of their problems – but most attorneys are not living and breathing eDiscovery on a day-to-day basis (and, frankly, don’t want to). For a variety of reasons, most attorneys don’t or can’t make time to research and learn about new technologies even when they’re faced with a discovery challenge. Even attorneys who do have the inclination and aptitude to deal with the mathematics and statistical requirements of a well-planned workflow, who understand how databases work, and who are unfazed by algorithms and other forms of AI, often don’t make the time to evaluate new technology because their plates are already full providing other services needed by their clients. And most attorneys became lawyers because they had little interest in mathematics, statistics, and other sciences, so they don’t believe they have the aptitude necessary to deal with eDiscovery (which isn’t really true). This means that when they’re facing gigabytes or even terabytes of data that have to be analyzed in a matter of weeks, they often panic. Many lawyers look for a way to make the problem go away. Sometimes they agree with opposing counsel not to exchange electronic data; other times they try to bury the problem with a settlement. Neither approach serves the client, who is entitled to an expeditious, cost effective, and just resolution of the litigation. Can you talk more about the service clients are entitled to, from an eDiscovery perspective? By that, I mean – can you explain the legal rules, regulations, and obligations that are implicated by eDiscovery, and how those may impact an attorney facing an electronic discovery request? Sure. Under Rule 1 of the FRCP and the laws of most, if not all, states, clients are entitled to a just resolution of the litigation. And ignoring most of the electronic evidence about a dispute because a lawyer finds dealing with it to be problematic rarely affords a client a just result. In many cases, the price the client pays for counsel’s ignorance is a surcharge to terminate the litigation. And, counsel’s desire to avoid the challenge of eDiscovery very often amounts to a breach of the ethical duty to provide competent legal services.The ABA Model Rules (as well as the ethical rules and opinions in the majority of states) also address the issue. The Model Rules offer a practitioner three alternatives when undertaking to represent a client in a case that involves ESI (which almost every case does). To meet his or her ethical obligation to provide competent legal services, the practitioner can: (1) become an expert in eDiscovery matters; (2) team up with an attorney or consultant who has the expertise; or (3) decline the engagement. Because comparatively few attorneys have the aptitude to become eDiscovery experts and no one who wants to practice law can do so by turning down virtually all potential engagements, the only practical solution for most practitioners is finding an eDiscovery buddy.In the end, I think attorneys are just looking for ways to make their lives (and thereby their clients’ lives) easier and they see eDiscovery as threatening to make their lives much harder. Fortunately, that doesn’t have to be the case.So, it sounds like you’re saying that despite the fact that it may cost them clients, there are sophisticated attorneys out there that are still eschewing legal technology and responding to discovery requests the way they did when most discovery requests involved paper documents? Absolutely there are. And I can empathize with their thought process, which is usually something along the lines of “I don’t understand eDiscovery technology and I’m facing a tight discovery deadline. I do know how to create PDFs from scanned copies of paper documents and redact them, if necessary. I’m just going to use the method I know and trust.” While this is an understandable way to think, it will immediately impose on clients the cost of inefficient litigation and settlements or judgments that could have been reduced or avoided if only the evidence had been gathered. Ultimately, when the clients recognize that their counsel’s fear of eDiscovery is imposing a cost on them, that attorney will lose the client. In other words, counsel who refuse to delve into ESI because it is hard is similar to a person who lost car keys in a dark alley but insists on only looking under the streetlight because it is easier and safer than looking in the dark alley.That’s such a great analogy. Do you have any real-world examples that may help folks understand the plight of an attorney who is basically trying to ignore ESI?Sure. Here’s a great example: Years ago, my good friend and partner told me he would retire without ever having to learn about eDiscovery. My partner is a very successful attorney with a great aptitude for putting clients at ease. But about a week after expressing that thought, he came to me with 13 five-inch three-ring binders. He wanted help finding contract paralegals or attorneys to prepare a privilege log listing all the documents in the binders. An arbitrator had ordered that if he did not have a privilege log done in a week, his expert would not be able to testify. His “solution” was to rent or buy a bunch of dictating machines and have the reviewers dictate the information about the documents and pay word processers overtime to transcribe the dictation into a privilege log. I asked what was in the binders. Every document was an email thread and many had families. My partner had received the data as a load file, but he had the duplications department print the contents rather than put them into a review platform. Fortunately, the CD on which the data was delivered was still in the file.I can tell this story now because he has since turned into quite the eDiscovery evangelist, but that is exactly the type of situation I’m referring to: smart, sophisticated attorneys who are just trying to meet a deadline and stay within budget will do whatever takes to get the documents or other deliverable (e.g., a privilege log) out the door. And without the proper training, unfortunately, the solution is to throw more bodies at the problem – which invariably ends up being more costly than using technology properly.Can you dive a bit deeper there? Explain how performing discovery the old-fashioned way on a small case like that would cost more money than performing it via a dedicated eDiscovery technology.Well, let me finish my story and then we’ll compare the cost of using 20th and 21st Century technologies to accomplish the same task. As I said, when I agreed to help my partner meet his deadline, I discovered all the notebooks were filled with printed copies of email threads and attachments. My partner received a load file with fewer than 2 GBs and gave it to the duplications department with instructions to print the data so he could read it. We gave the disk to an eDiscovery provider, and they created a spreadsheet using the email header metadata to populate the log information about who the record was from, who it was to, who was copied, whether in the clear or blind, when it was created, what subject was addressed, etc. A column was added for the privilege(s) associated with the documents. Those before a certain date were attorney-client only. Those after litigation became foreseeable were attorney-client and work product. That made populating the privilege column a snap once the documents were chronologically arranged. The cost to generate the spreadsheet was a few hundred dollars. Three in-house paralegals were able to QC, proofread, and finalize the log in less than three days for a cost of about $2,000.Had we done it the old-fashioned way, my partner was looking at having 25 or 30 people dictating for five days. If the reviewers were all outsourced, the cost would have been $12,000 to $15,000. He planned to use a mix of in-house and contract personnel - so, the cost would have been 30% to 50% higher. The transcription process would have added another $10,000. The cost of copying the resulting privilege log that would have been about 500 pages long with 10 entries per page for the four parties and arbitrator would have been about $300. So even 10 years ago, the cost of doing things the old-fashioned way would have been about $35,000. The technology-assisted solution was about $2,500. Stay tuned for the second blog in this series, where we delve deeper into how attorneys can save their clients money, achieve better outcomes, and gain more repeat business once they overcome common misconceptions around eDiscovery technology and costs. If you would like to discuss this topic further, please reach out to Casey at cvanveen@lighthouseglobal.com and Gordon at Gordon.Calhoun@lewisbrisbois.com.ediscovery-review; ai-and-analyticsediscovery-process, blog, spectra, law-firm, ediscovery-review, ai-and-analyticsediscovery-process; blog; spectra; law-firmcasey van veen

Whether it’s called DIY eDiscovery, SaaS eDiscovery, or self-service, spectra eDiscovery, one thing is clear—everyone in the legal world is interested in putting today’s technologies to work for them to get more done with less. It’s a smart move, given that many legal teams are facing an imbalance between needs and resources. As in-house legal budgets are being slashed, actual workloads are increasing.Now more than ever, legal teams need to ensure they’re choosing and using the right tools to effectively manage dynamic caseloads—a future-ready solution capable of supporting a broad range of case types at scale. Given the variety of options on the market, it’s understandable there’s some uncertainty about what to pursue, let alone what to avoid. Below, I have outlined guidance to help your legal team navigate the top three potential pitfalls encountered when seeking a self-service, spectra eDiscovery solution.1. Easy vs. PowerfulThere are a lot of eDiscovery solutions out there making bold promises, but many still force users to choose between ease of use and full functionality. While a platform may be simple to learn and navigate, it may fail to offer advanced features like AI-driven analysis and search, for example.Think of it like the early days of cell phones, when we were forced to choose between a classic brick-style device or a new-to-market smartphone. Older phones were easy to use, offering familiar capabilities like calling and text exchange, while newer smartphones provided impressive, previously unknown functionalities but came with a learning curve. With the advancement of technology, today’s device buyers can truly have it all at hand—a feature-rich mobile phone delivered in an intuitive user experience.The same is true for dynamic eDiscovery solutions. You shouldn’t have to choose between power and simplicity. Any solution your team considers should be capable of delivering best-in-class technology over one simple, single-pane interface.2. Short-Term Thinking vs. Long-Term Gains As organizations move to the seemingly unlimited data storage capacities of cloud-based platforms and tools, legal teams are facing a landslide of data. Even the smallest internal investigation may now involve hundreds of thousands of documents. And with remote working being the new global norm, this trend will only continue to grow. Legal teams require eDiscovery tools that are capable of scaling to meet any data demand at every stage of the eDiscovery process.When evaluating an eDiscovery solution, keep the future in mind. The solution you select should be capable of managing even the most complex case using AI and advanced analytics—intelligent functionality that will allow your team to efficiently cull data and gain insights across a wide variety of cases. Newer AI technology can aggregate data collected in the past and analyze its use and coding in previous matters—information that can help your team make data-driven decisions about which custodians and data sources contain relevant information before collection. It also offers the ability to re-use past attorney work product, allowing you to save valuable time by immediately identifying junk data, attorney-client privilege, and other sensitive information.3. Innovation vs. UpkeepThanks to the DIY eDiscovery revolution, your organization no longer has to devote budget and IT resources to upkeeping a myriad of hardware and software licenses or building a data security program to support that technology. Seek a trusted solution provider that can take on that burden with development and security programs (with the requisite certifications and attestations to prove it). This should include routine technology assessment and testing, as well as using an approach that doesn’t disrupt your ongoing work.As you’re asked to do more with less, the right cloud-based eDiscovery platform can ensure your team is able to meet the challenge. By avoiding the above pitfalls, you’ll end up with a solution that’s able to stand up against today’s most complex caseloads, with powerful features designed to improve workflow efficiency, provide valuable insights, and support more effective eDiscovery outcomes.If you’re interested in moving to a DIY eDiscovery solution, check out my previous blog series on self-service, spectra eDiscovery for corporations, including how to select a self-service, spectra eDiscovery platform, tips for self-service, spectra eDiscovery implementation, and how self-service, spectra eDiscovery can make in-house counsel life easier. ediscovery-review; ai-and-analyticsself-service, spectra, ediscovery-process, corporation, prism, blog, spectra, corporate, ediscovery-review, ai-and-analyticsself-service, spectra; ediscovery-process; corporation; prism; blog; spectra; corporatelighthouse

The approach of a new year is often a good time to step back and take stock of the eDiscovery industry, so that we can be better prepared to move forward. One of the most dramatic changes over the past few years has been the seismic shift across the legal and corporate data landscapes. That shift has slowly been expanding the concept of eDiscovery beyond a single-litigation focus, to encompass data governance, data privacy and security, and an overall more holistic, strategic approach to review and analysis.As we prepare to move forward in this brave new world, it’s important to understand how those industry changes affect the traditional framework of the eDiscovery process: the Electronic Discovery Reference Model (EDRM). Recently, I was lucky enough to join a panel of industry experts, including Microsoft’s EJ Bastien, TracyAnn Eggen from CommonSpirit Health, and Lighthouse’s Sarah Barsky-Harlan, to dive deeper into that specific issue. Together, we tackled questions like: Does the EDRM still apply in today’s more complex eDiscovery environment? If so, how is the evolving data and eDiscovery landscape reshaping how organizations and law firms think about the EDRM? How can the EDRM be used to meet today’s more complex communication, data, and business challenges?Below are some of the key themes and ideas that emanated from that discussion: A Brave New Data World: Dynamic Changes in eDiscoverySince its inception, the EDRM has been the industry’s standard approach to the eDiscovery process (i.e., identification, collection, processing, review, analysis, and production of electronically stored information (ESI)). However, what we’re seeing today is that organizations and law firms now must think about eDiscovery in much broader terms than that traditionally very linear method. There are three primary reasons for this change:New cloud-based and Software as a Service (SaaS) systems: Enterprise systems are not nearly as controlled by the underlying organization as they used to be. Even five years ago, IT departments could more closely manage what software was installed, as well as when, how, and what upgrades were rolled out. Now those updates and installations are managed by cloud providers, with upgrades rolling out on an almost weekly basis – often with no notice to the organization. All those changes have downstream eDiscovery impacts, which must be dealt with at each stage of the EDRM process.New data formats: Data is no longer structured in the traditional document “family” of an email parent with attachment children. The shift to chat and collaboration platforms within organizations means that communications and workflows generate more data across multiple data sources and are much more fluid and informal. For instance, instead of an employee working on a static document saved on a desktop and then passing that document back and forth to co-workers via email, those employees may work on that document together while it’s saved on a cloud-based collaboration platform, chat about it via an in-office chat application, post updates on it via the collaboration tool channel, as well as email copies back and forth to each other. This means counsel must analyze how relevant data ties together and analyze the relationships between data sources in order to understand the full story of a communication during an investigation or litigation.New capabilities with eDiscovery technology: There are many new types of capabilities that are native to enterprise systems, as well as new types of analytics and artificial intelligence (AI) that can handle more data at scale. These new capabilities are allowing case teams to leverage past data on new cases and get to key data more quickly in the EDRM process. The Impact: How Those Changes Affect the EDRM FrameworkThinking of the EDRM as a monolithic linear process that flows straight from beginning (collection) to end (production) does not fit the way eDiscovery takes place in practice anymore. There is a world of complexity within each step of the EDRM – one that is highly dependent on the data source. And the decisions made along the way for each data source at each new step will impact what happens next – often in a non-linear fashion: Sometimes that next step will send practitioners back to collection again, because they found another data source during review. Sometimes review takes place simultaneously with collection or processing phases, depending on the data source and those newer capabilities discussed above. In short, the old model of collecting all data, exporting it all, and then reviewing it all, in large chunks, one step at a time, is no longer applicable nor practical.Instead, a “mini-EDRM” framework might make more sense, where organizations prepare workflows for the preservation, collection, processing, and review of each particular data source. Thinking of the EDRM in this way also helps the framework stay relevant and future-proof as practitioners deal with the sea-change happening across our data landscape. Practitioners need to be agile enough to handle new data sources as they pop up, for each step of the EDRM process, and then be prepared to do it all over again when someone in a deposition mentions another new data source, and to adapt it when something changes in the data source. A mini-EDRM framework would help organizations and practitioners better meet those challenges.The EDRM and Data-in-PlaceAs noted above, the eDiscovery process is now much broader and has much more of an impact on organizational information governance and data-in-place than ever before. This presents an opportunity to use learnings from across the EDRM to more effectively manage data “to the left” of that traditional process. For example, if a particular data source was problematic during review, that information can be disseminated at the organizational level and help inform how that source is used within the organization moving forward. Or if practitioners notice a large volume of irrelevant data during review that shouldn’t exist in the system at all, that information can be used to redraft document retention policies. In this way, eDiscovery (and the EDRM framework) can now be a force for change over the entire organization.Thinking Beyond a Single MatterIn today’s more dynamic and voluminous data landscape, the work we did in the past is more valuable than ever before and it can be used to inform and impact current processes across the EDRM.This can come in the form of people and institutional knowledge: experienced and consistent staff and outside partners are an invaluable resource. These organizational experts can use their understanding and experience with an organization’s past matters, system architecture, data sources, workflows etc. to improve eDiscovery efficiency and solve current problems more effectively. It can also come in the form of technology: when the EDRM first evolved, data analytics were a much heavier lift. The process and tools were expensive and the amount of data that they could be applied to was much smaller than today. Advancements in AI capabilities now allow us to analyze much larger volumes of data with much more accurate results. Thus, this newer, advanced AI technology is now capable of leveraging the goldmine of millions of previous decisions made by attorneys on an organization’s past matters. That work product is baked into the data, and advanced AI can use it to make more accurate decisions on current data at a much larger scale than ever before.Tips to Keep the EDRM Applicable in an Evolving Data LandscapeStrive to retain institutional knowledge across matters: The constantly evolving eDiscovery landscape makes continuity and retaining institutional knowledge incredibly important. Starting from scratch each time you confront a new data source or problem along the EDRM is no longer practical with today’s diversified and larger data volumes. Work to cultivate valuable partners and staff who will work to understand your organization’s data architecture, as well as the eDiscovery workflows that are effective within your environment.Lean on your peers: Chances are, if you’re facing a problem with a challenging data source at one stage of the EDRM, someone in your peer group has also faced the same or a similar problem. Don’t be afraid to reach out and ask folks to benchmark. Peer experience can help each practitioner learn and move forward, solving challenging industry problems along the way.Open the lines of communication: Because the EDRM process is much more iterative and each step impacts other steps, it is incredibly important that the people working on those steps do not work in silos. Everyone should know the downstream impacts of their decisions and workflows.Test… and test again: Employ a testing framework to test the impact of eDiscovery workflows on the underlying platforms, and then have a feedback loop to apply changes. This will ensure your eDiscovery program is forward-thinking, as opposed to reactive. Automate where possible: When striving for repeatable, defensible eDiscovery processes, predictability is key. And automation, when feasible, is a great way to achieve that predictability. Automating workflows across the EDRM will not only help improve efficiency and lower costs, it will also help minimize risk and keep your eDiscovery program defensible.information-governance; ediscovery-review; chat-and-collaboration-datacloud, analytics, information-governance, ediscovery-process, blog, information-governance, ediscovery-review, chat-and-collaboration-data,cloud; analytics; information-governance; ediscovery-process; bloglighthouse

Let’s begin by setting the stage. You’ve evaluated the ways a self-service, spectra eDiscovery solution could benefit your organization and determined the approach will help you boost workflow efficiency, free up internal resources, and reduce eDiscovery practice and technology costs. You’ve also researched how to ideally implement a solution and armed yourself with strategies to build a business case and overcome stakeholder objections that may arise.You’re now ready to move on to the next step in your organization’s self-service, spectra eDiscovery journey: selecting the right solution provider. When it comes to selecting a solution provider, one size does not fit all. Every organization has different eDiscovery needs—including yours—and those needs evolve. From how attorneys and eDiscovery teams are structured within the organization and their approach to investigations and litigations, to the types of data sources implicated in those matters and how those matters are budgeted—there’s a lot to be considered.The self-service, spectra solution you choose should be able to adapt to your changing needs and grow with your organization. Below, I’ve outlined four key considerations that will help you select a fitting self-service, spectra solution for your organization.1. Is the solution capable of scaling to handle any matter? ‍It’s important to select a self-service, spectra eDiscovery solution capable of efficiently handling any investigation or litigation that comes your way. A cloud-based solution can easily, swiftly scale to handle any data volume.You’ll also want to ensure your solution can handle the type of data your organization routinely encounters. For example, collecting, processing, and reviewing data generated by collaborative applications like Microsoft Teams may require special tools or workflows. The same can be said for data generated by chat messages or cellphone data. Before selecting a self-service, spectra solution, you’ll benefit from outlining the types of data your organization must handle and asking potential solution providers how their platform supports each.Additionally, you may be interested in the ability to move to a full-service model with your provider, should the need arise. With scalable service, your team will have access to reliable support if a matter become too challenging to manage in house. With a scalable solution bolstered by a flexible service model, your organization can bring on help as needed, without disruption. 2. Does the solution drive data reduction and review efficiency across the EDRM?‍Organizational data volumes are increasing year after year—meaning even small, discrete internal investigations can quickly balloon into hundreds of thousands of documents. Collecting, processing, analyzing, and producing large amounts of data can be costly, complicated, time consuming, and may open up your organization to legal risk if the right tools and workflows are not in place.Look for a self-service, spectra solution capable of managing data at scale, with the ability to actively help your organization reduce its data footprint. This means choosing a provider that can offer expert guidance around data reduction techniques and tools. Ask potential solution providers if they have resources to address the cost burden of data and mitigate risk through strategies like defensible data collections, effective search term selection, or crafting early case assessment (ECA), and technology assisted review (TAR) workflows.The provider should also be able to deliver technology engineered to reduce data resource draw, like processing that allows access to data faster, tools to cut down on hosted review data volume, and AI and analytics that provide the ability to re-use attorney work product across multiple matters. In short, seek a self-service, spectra solution that gives your organization the ability to defensibly and efficiently reduce the amount of costly human review across your organization’s portfolio. 3. Will the solutions’ pricing model align to your organization’s changing needs? Your organization’s budget requirements are unique and will likely change over time. Look for a solution provider that can change in accord and offer a variety of pricing models to fit your budgetary requirements. Ask prospective providers if they are able to design pricing around your organization’s expectations for utilization. Modern pricing models can be flexible yet predictable to prevent unexpected charges or overages, and ultimately align to your organization’s financial needs.4. Is the solution’s roadmap designed to take your organization into the future? When selecting a self-service, spectra solution it’s easy to focus on your current needs, but it’s equally important to consider what a self-service, spectra solution provider has planned for the future. If a vendor is not forward thinking, an organization may find itself being forced to used outdated technology that’s not able to take on new security challenges or process and review emerging data sources.Pursue a provider that demonstrates the ability to anticipate market trends and design solutions to address them. Ask potential providers to articulate where they see the market moving and what plans they have in place to update their technology and services to reflect what’s new. It can be helpful to question if a provider’s roadmap aligns to your organization’s direction. For example, if you know your company is planning to make a systematic change, like moving to a bring your own device (BYOD) policy or migrating to the cloud, you’ll want to confirm the self-service, spectra solution can support that change. Asking these types of questions before selecting a provider will guarantee the solution you choose will be able to grow with both your organization and the eDiscovery industry as a whole. With awareness and understanding of the true potential offered in a self-service, spectra solution, you can ultimately choose a provider that will help you level up your organization’s eDiscovery program. ediscovery-review; ai-and-analyticsself-service, spectra, blog, ediscovery-review, ai-and-analyticsself-service, spectra; bloglighthouse

In my last two blogs, I discussed how your legal department can productize services to become more efficient as well as shared some tips for how to determine the legal needs within your organization. Now that you know the added benefits and understand the legal needs, the natural next step is to determine what legal service “products” to offer, as well as any gaps. However, if nobody knows what these repeatable solutions are, what good are they? This is where creating an internal marketing plan to get the word out about your department’s legal services is critically important. In this blog, we’ll talk about how to do that by answering who, what, when, where, and why.Who?When you create your internal plan, the first thing you need to do is understand who you are marketing to. The easiest way to do this is to create some simple “personas.” You can easily do this based on the interviews you conducted as part of your earlier search. You should build a persona for each distinct type of user coming to you – typically this aligns with internal departments. In detailing each persona, you should include the following:Typical day-to-day work of your personaTypical interaction with legalTop of mind issues/challengesOther notesWhat?Next, you will need to decide what you are going to market to these personas (i.e repeatable workflows). Common ones in the legal arena are contract, litigation, HR investigation, and patent workflows. Once you have the workflows applicable to your company identified, detail the features of each workflow. For example, it is automated; has six common template documents, a clause library, and contract status; and leverages existing company technology.Once you have your personas, workflows, and features, you’re ready to create a positioning document. You should create one document for every problem/solution set (i.e. workflow). This will form the basis of how you share the information with others. The goal of this document is to position your solution in a way that resonates with the internal users. Below is a format that I find helpful to follow and I have inserted an example based on a contract workflow.PROBLEM: There is a problem in the company today. Contract negotiations are long, cumbersome, and not transparent. This can delay revenue opportunities. In addition, final contracts are difficult to locate and manage.SOLUTION: The ideal solution to this problem is an easy-to-use process, with some contracts being able to avoid legal review. The solution would allow easy access to status for interested parties and would allow those, or other, interested parties to access the contractual information at a later date.PRIMARY MESSAGE (SHORT - 1 SENTENCE): The Corporate Legal Department delivers a business-driven model for negotiating and managing contracts that accelerates, not hinders, company growth.SERVICE DESCRIPTION (2-3 SENTENCES): By leveraging an intake form, employees are directed to a self-service, spectra portal for template contracts or put in touch with an attorney for more complex matters. The status of their request, as well as information about all finalized contracts, is displayed in our JIRA system giving users full access to contract status as well as important contractual data of finalized contracts.HIGHLIGHTS (THESE SHOULD BE PROBLEM-ORIENTED FEATURES):Reduces contract turnaround by leveraging templated contracts and clausesAllows users access to contract status anytime, anywhereNo new systems (i.e. leverages existing company tools)Etc.The above will create a lot of different worksheets and information. Since I like to keep things a little simpler, I also create a cliff notes version of this to show the all-up view of your corporate legal department’s services.Once you have completed your positioning, don’t be afraid to run the messaging by some of the people you interviewed. You want to make sure that it is clear how legal will be helping them get their work done. I would suggest selecting people who are friendly to your department and who you have a good working relationship with since you are running draft information by them and not a final product.Where, When, and Why?Third, you need to think about where, when, and why you are getting the message out. The goal is to get it out wherever your users are, often, and in a way that they like to consume the information. At a minimum, I would suggest doing a launch of the updated services and including information about that launch on:The company wiki page/internal siteAny internal ticketing toolA company newsletter (or a company meeting if appropriate)Any onboarding materials/presentations your company does for new hiresOr even a “roadshow,” where you present to each department within your organization what services the legal team offersDuring any presentation, it is always helpful to inject some fun into the presentation. I have heard of some legal departments doing humorous videos or skits to capture the attention of their employees. Partner with your internal marketing team, as they may have some great suggestions on how you can get the word out.Finally, don’t forget about post-launch messaging. Though you may see an uptick in users after a launch, some people will have missed the information the first time around or will have forgotten it by the time they get to an issue that they want to bring to legal. To that end, make sure you have a plan for continued marketing. I like to showcase successes in follow-up marketing (e.g. a contract turnaround case study showing the reduced times or some metrics on impact). This information can be shared in an employee newsletter or as a quick email to leaders asking them to share it in their department meetings.This is quite a robust process and you should expect it will take several weeks, or even months, to complete. You will also likely continue to refine this marketing plan as you address gaps by adding services and gathering feedback. The benefit of going through this process is that it brings clarity to what legal does, brings efficiency by advertising repeatable workflows, and gives everyone in legal visibility into the challenges in the business and how legal addresses those.legal-operationslegal-ops, blog, legal-operations,legal-ops; bloglighthouse

eDiscovery is currently undergoing a fundamental sea change, including how we think about data governance and the EDRM. Linear review and older analytic tools are quickly becoming outdated and unable to handle modern datasets, i.e., eDiscovery datasets that are not only more voluminous than ever before, but also more complicated – emanating from an ever-evolving list of new data sources and steeped in variety of text and non-text-based languages (foreign language, slang, emojis, video, etc.).Fortunately, technological advancements in AI have led to a new class of eDiscovery tools that are purpose built to handle “big data.” These tools can more accurately identify and classify responsiveness, privileged, and sensitive information, parse multiple formats, and even provide attorneys with data insights gleaned from an organization’s entire legal portfolio.This is great news for legal practitioners who are faced with reviewing and analyzing these more challenging datasets. However, evaluating and selecting the right AI technology can still present its own unique hurdles and complexities. The intense purchasing process can raise questions like: Is all AI the same? If not, what is the difference between AI-based tools? What features are right for my organization or firm? And once I’ve found a tool I like, how do I make the case for purchasing it to my firm or organization?These are all tough questions and can lead you down a rabbit hole of research and never-ending discussions with technology and eDiscovery vendors. However, the right preparation can make a world of difference. Leveraging the below steps will help you simplify the process, obtain answers to your fundamental questions, and ultimately select the right technology that will help you overcome your eDiscovery challenges and up level your eDiscovery program.1. Familiarize Yourself with Subsets of AI in eDiscoveryNewer AI technology is significantly better at tackling today’s modern eDiscovery datasets than legacy technology. It can also provide legal teams with previously unheard-of data insights, improving efficiency and accuracy while enabling more data-driven strategic decisions. However, not all technology is the same – even if technology providers tend to generally refer to it all as “AI.” There are many different subsets of AI technology, and each may have vastly different capabilities and benefits. It’s important to understand what subsets of AI can provide the benefits you’re looking for, and how those different technology subsets can work together. For example, Natural Language Processing (NLP) enables an AI-based tool to understand text the same way that humans understand it – thus providing much more accurate classifications results – while AI tools that leverage deep learning technology together with NLP are better able to handle large and complex datasets more efficiently and accurately. Other subsets of AI give tools the ability to re-use data across matters as well as across entire legal portfolios. Learning more about each subset and the capability and benefits they can provide before talking to eDiscovery vendors will give you the knowledge base necessary to narrow down the tools that will meet your specific needs. 2. Learn How to Measure AI ROIAs a partner to human reviewers, advanced AI tools can provide a powerful return on investment (ROI). Understanding how to measure this ROI will enable you to ask the right questions during the purchasing process to ensure that you select a tool that aligns with your organization or law firm’s priorities. For example, if your team struggles with review accuracy when utilizing your current tools and workflows, you’ll want to ensure that the tool you purchase is quantifiably more accurate at classifying documents for responsiveness, privilege, sensitive information, etc. The same will be true for other ROI metrics that are important to your team, such as lower overall eDiscovery spend or increased review efficiency.These metrics will also help you build a strong business case to purchase your chosen tool once you’ve selected it, as well as a verifiable way to confirm the tool is performing the way you want it to after purchase.3. Come Prepared with a List of QuestionsIt’s easy to get swept up in conversations about tools and solutions that end without the metrics you need. A simple way to control the conversation and ensure you walk away with the information you need is to prepare a thorough list of questions that reflect your priorities. Also be sure to have a method to record each vendor’s response to your questions. A list of standard questions will keep conversations more productive and provide a way to easily contrast and compare the technology you’re evaluating. Ensure that you also ask for quantifiable metrics and examples to back up responses, as well as references from clients. This will help you verify that vendor responses are backed by data and evidence.4. Know the Pitfalls of AI Adoption—and How to Avoid ThemIt won’t matter how much you understand AI capabilities, whether you’ve asked the right questions, or whether you understand how to measure ROI, if you don’t know how to avoid common AI pitfalls. Even the best technology will fail to return the desired results if it’s not implemented properly or effectively. For example, there are some workflows that work best with advanced AI, while other workflows may fail to return the best results possible. Knowing this type of information ahead of time will help you get your team on board early, ensure a smooth implementation, and enable you to unlock the full potential of the technology.These tips will help you better prepare for the AI purchasing process. For more information, be sure to download our guide to buying AI. This comprehensive guide offers a deep dive into tips and tactics that will help you fully evaluate potential eDiscovery AI tools to ensure you select the best tool for your needs. The guide can also be used to reevaluate your current AI and analytic eDiscovery tools to confirm you’re using the best available technology to meet today’s eDiscovery challenges.lighting-the-way-for-review; ai-and-analytics; ediscovery-review; lighting-the-path-to-better-review; lighting-the-path-to-better-ediscoveryreview, ai-big-data, blog, ai-and-analytics, ediscovery-reviewreview; ai-big-data; blogai-analyticssarah moran

For years, general counsel have weighed the pros and cons of doing a task internally versus sending the work to outside counsel – this is not a new dichotomy. What is newer, however, is the proliferation of technology available for legal and the business savvy now being applied to internal legal departments. This has opened up more choices for legal departments. First, you have to figure out whether you can apply technology, then whether you should build or buy that technology, and finally if you should outsource any portion of the process.Before you start down the path of buy vs. build vs. outsource, I would recommend assessing your department’s offerings. In the earlier parts of this series, I outline how you can do that. Once you understand your services and your gaps, you can better determine where you may need to apply build vs. buy decisions. Whether you are a general counsel or a legal operations professional, this blog will outline four key aspects to include in your framework as you make these decisions.1. Problem/Solution ListStart with a list of services your company needs and possible solutions. If you followed the productization process, you will have a good list. If you have not yet done this, you can at least jot down a list of your company’s legal needs, how pervasive and urgent they are, whether they further the company strategy, as well as any potential solutions.Next, order that list from most pervasive to least pervasive. Where there is a tie, look to the problem’s relationship to company strategy.Next, work through all of the items in box A. You want to be able to answer the following questions:Is there an existing solution?Is there a software solution that may apply?What are the costs/benefits of all possible solutions?Is there typically urgency around the request?All other things being equal, do we have the expertise to handle this in house?If you have gaps in A, B, or C, I would recommend addressing those before process improvement items.2. Cost-Benefit AnalysisNext, for any change (either addressing a gap or a process improvement) you should do a cost-benefit/return on investment analysis. Note that if you are just trying to get a sense of which problem on your list to address, you can do a high-level analysis by categorizing the solutions into low, medium, or high financial impact. If, however, you are getting to the point of suggesting a change internally and asking for budget, you want to do a much more in-depth quantitative analysis. On the benefit side, you want to consider any revenue acceleration for the company (e.g., customers’ revenue hits a quarter earlier) as well as costs reduced and avoided (e.g. outside counsel fees). If there are other quantifiable benefits, you should include them as well. On the expense side, make sure to consider licensing, annual maintenance, user fees, implementation, infrastructure, training, hourly support/expert charges, and any ongoing costs. You should predict these benefits and costs for the next 3 years, as that is a common period to see whether there is a return on your investment. You can also prepare a version of this document showing the same cost/benefit of building the solution internally as well as outsourcing it to outside counsel.3. Additional Factors: Urgency and ExpertiseOnce you have the cost-benefit analysis for the various solutions, you usually have a preferred direction. However, don’t forget to account for time and expertise. You should then consider how urgent the requests are. The more urgent a request, the more likely it should be handled by technology or outsourced, as those solutions typically can bring more resources to bear. You should then consider expertise. More specifically, does one need specific knowledge about the company to solve this problem or will there be a lot of need to liaise internally? If so, the solution should likely stay with the internal corporate legal department. Conversely, does this require niche expertise and is it better handled by an outside counsel with that expertise? Make notes of these considerations with your cost-benefit analysis, as these factors can sway a decision in one direction or another.4. Decision TimeUltimately, making these decisions is more of an art than a science. They are also decisions that can and should be revisited as things change in your business and legal department. The above should give you the right information to make an informed decision. Ultimately, you will want to share your decision with others and get input before finalizing a direction.By following the productization process, orienting your solutions towards your customers, streamlining how you deliver services, and applying the right sets of resources through build versus buy decisions, your legal department will operate more efficiently. legal-operationslegal-ops, blog, legal-operations,legal-ops; bloglighthouse

Many law departments are reactionary. Someone comes to legal with a “legal” question and they help that person. Although this makes a lot of sense, as legal is a support department, it makes it very difficult to thematically explain the value legal is driving as well as understand the work the department is doing. As legal operations matures and legal departments look to be more efficient, productizing the services in the department is a natural progression. This approach was a central discussion at the 2021 CLOC conference and the subject of this blog series. In order to productize something effectively, however, you need a very good understanding of your customer and prospective customers’ needs. In this article, I will give you an overview of how to get that.A central theme in product management is building resonators – products that resonate with the buyers. You may have the best idea but, if it doesn’t meet a pervasive market need, nobody will buy it. There are many great examples of products that failed and dozens of lessons we can learn from those failures. Most of the lessons come back to misunderstanding the customer's need and the nature of that need. For example, people may say they want a better mousetrap but if you don’t ask how much they would pay for that mousetrap, whether they would replace any current mousetraps with a better one, and whether it matters if the new mousetrap gives off an odor of chemicals, you can see how you might not make a best seller. To give an example in the legal services space, in my first general counsel role, I heard from many people how it was frustrating that they could never find contracts when they needed them. I immediately set upon a mission to create a contracts database. After investing a lot of time, we had a wonderfully organized database, and the only person who ever used it was the legal team. So what happened to all the frustrated employees from other departments? It turns out I didn’t ask them how often they needed to look up contracts and whether that need was part of another legal request (meaning that legal was the one actually looking up the contract anyway). In the end, the contract database was extremely helpful for the legal department but I could have saved myself the time of making it self-service, spectra and figuring out permissions for different users had I asked some questions upfront. To avoid the same fate, there are four principles you can use when asking your company about its legal needs.1. Don’t rely on the users to define the needs. Instead, be curious about their day-to-day and in that curiosity, you will be able to see the legal needs. The theory is this: if you ask someone what they need from legal, they will overlay their belief system about what legal should provide before they answer. Instead, when you ask them about their role, their goals, how they are measured, and what their biggest challenges are, you are more likely to be able to understand them and see where legal may be able to help.2. Create a template interview form and use it religiously with each person.When you do 10-15 interviews, you want to be able to discern themes and compare interviews. When multiple people are conducting interviews, you want to be sure you are all hitting the same topics. This is much easier to do when you start from a template. For a 30-minute interview, I would suggest 3-5 template questions. Always get background information before the interview starts including their name, title, department, and contact information. Put this information at the top of your interview summary. Do not include this in your 3-5 questions. Having this information clearly labeled and available allows you to easily follow up later. Next, move on to background and devote 2-3 questions to this area including what are their main goals for the year, how is their department measured, what are their biggest pain points. Finally, go on to any specific areas you may want to ask about. For example, you may want to know how they have used the legal department in the past, how much they interact with overseas colleagues, etc. Here is a list of common questions:What are your department’s goals for the year?How is your department measured?What are your biggest roadblocks in achieving your goals?What are your biggest roadblocks in getting your job done?If you had a magic wand and could change one thing about your job, what would it be?What are your most common needs outside your department?What is your perception of what the legal department does?What kinds of things have you come to legal for?3. Interview a diverse group. It may seem obvious that you need a good sample size, however, you will be surprised at how varied the needs are at different levels and across different departments. If you are only interviewing one person to represent a specific level or department, you should ask them “how representative do you think your pain points/goals are of the department?” This will give you a good idea of whether you can rely on this person’s interview as representative of the department or whether you will have to do some follow-up interviews with others.4. Always ask follow-up questions.The guidance for limiting your template to 3-5 questions above ensures you have time for follow up on each response. More specifically, you want to be sure you are really understanding the responses and quantifying the level and frequency of any relevant pain points. I would set a goal to ask 2 follow-up questions for every first response. For example, if your first question is “what are your goals for 2021?” then you should expect to ask 2 follow-up questions after your interviewee responds. If at any point the person you are interviewing mentions a challenge that you think legal can help to solve, this is your queue to follow up around the pain and pervasiveness. Here are some questions you can ask to get into how big a problem they are facing:How often do you run into this roadblock: daily, weekly, monthly, quarterly?When you run into this roadblock, how much time do you spend resolving it: 1-2 hours, 2-5 hours, 5-10 hours, 10+ hours?Does this roadblock impact multiple people? If so, how many?Does this roadblock (or a stoppage in you moving towards your goals) impact other departments?Are there workarounds for this roadblock? If so, how cumbersome are they on a scale of 1-5?If you had to reach out to another department and work with someone to remove this roadblock each time it came up, would you do that or would you continue with the workaround?How long would you wait for an outside resource to help before you proceed with your current workaround?Does the challenge have an impact on revenue?Whether you are a general counsel just getting to know your organization, a legal operations professional tasked with making your department more efficient, or a lawyer who is interested in ensuring you are providing great services, the above should give you a good place to start to understand your customer. Once you understand your customer, you’re able to provide great resonating services and position your existing solutions. legal-operationslegal-ops, blog, legal-operations,legal-ops; bloglighthouse

Artificial intelligence (AI), advanced analytics, and machine learning are no longer new to the eDiscovery field. While the legal industry admittedly trends towards caution in its embrace of new technology, the ever-growing surge of data is forcing most legal professionals to accept that basic machine learning and AI are becoming necessary eDiscovery tools.However, the constant evolution and improvement of legal tech bestow an excellent opportunity to the forward-thinking eDiscovery legal professional who seeks to triumph over the growing inefficiencies and ballooning costs of older technology and workflow models. Below, we’ll provide you with arguments to pull from your quiver when you need to convince Luddites that leveraging the most advanced AI and analytics solutions can give your organization or law firm a competitive and financial advantage, while also reducing risk.Argument 1: “We already use analytical and AI technology like Technology Assisted Review (TAR) when necessary. Why bring on another AI/analytical tool?”Solutions like TAR and other in-case analytical tools remain worthwhile for specific use cases (for example, standalone cases with massive amounts of data, short deadlines, and static data sets). However, more advanced analytical technology can now be used to provide incredible insight into a wider variety of cases or even across multiple matters. For example, newer solutions now have the ability to analyze previous attorney work product across a company’s entire legal portfolio, giving legal teams unprecedented insight into institutional challenges like identifying attorney-client privilege, trade secret information, and irrelevant junk data that gets pulled into cases and re-reviewed time and time again. This gives legal teams the ability to make better decisions about how to review documents on new matters.Additionally, new technology has become more powerful, with the ability to run multiple algorithms and search within metadata, where older tools could only use single algorithms to search text alone. This means that newer tools are more effective and efficient at identifying critical information such as privileged communications, confidential information, or protected personal information. In short, printing out roadmap directions was advanced and useful at the time, but we’ve all moved on to more efficient and reliable methods of finding our way.Argument 2: “I don’t understand this technology, so I won’t use it” This is one of the easiest arguments to overcome. A good eDiscovery solution provider can offer a myriad of options to help users understand and leverage the advances in analytics and AI to achieve the best possible results. Whether you want to take a hands-off approach and have a team of experts show you what is possible (“Here are a million documents. Show me all the documents that are very likely to be privileged by next week”), or you want to really dive into the technology yourself (“Show me how to use this tool so that I can delve into the privilege rate of every custodian across multiple matters in order to effectuate a better overall privilege review strategy”), a quality solution provider should be able to accommodate. Look for providers that offer training and have the ability to clearly explain how these new technologies work and how they will improve legal outcomes. Your provider should have a dedicated team of analytics experts with the credentials and hands-on experience to quell any technology fears. Argument 3: “This technology will be too expensive.”Again, this one should be a simple argument to overcome. The efficiencies that the effective use of AI and analytics achieve can far outweigh the cost to use it. Look for a solution provider that offers a variety of predictable pricing structures, like per gig pricing, flat fee, fees generated by case, fees generated across multiple cases, or subscription-based fees. Before presenting your desired solution to stakeholders, draft your battle plan by preparing a comparison of your favored pricing structure vs. the cost of performing a linear review with a traditional pricing structure (say, $1 per doc). Also, be sure to identify and outline any efficiencies a more advanced analytical tool can provide in future cases (for example, the ability to analyze and re-use past attorney work product). Finally, when battling against risk-averse stakeholders, come armed with a cost/benefit analysis outlining all of the ways in which newer AI can mitigate risk, such as by enabling more accurate and consistent work product, case over case.ai-and-analyticsanalytics, ai-big-data, blog, ai-and-analytics,analytics; ai-big-data; bloglighthouse

The 2021 CLOC conference focused a lot on applying product principles to legal services. General Counsel are often in the position of having to show the value of their team’s services and why, as a cost center, it makes sense to continue to grow their department or to buy technology to support their department. In addition to showing that value, there is pressure to be more efficient while providing excellent customer services. By productizing services, you can provide repeatable, measurable solutions that address the needs above. There is also the great benefit of being connected to your client’s needs by providing the services that match the most pervasive and urgent needs. However, if you don’t have a background in product management, how does one go about productizing legal services, and what does that even mean? As someone who is Pragmatic Marketing Certified through the Pragmatic Institute, I am here to help. This blog, and the blog series to follow, will show you how to get started, interview people internally to understand the needs, position your existing solutions internally, and make build vs. buy vs. outsourcing decisions. Let’s start with a high-level overview of where to begin.What does productizing legal services mean? Productizing your legal services focuses on creating solutions that apply to multiple customers in a repeatable way. This means that you first have to understand your customers’ problems by listening, asking, and observing. It then means that you create several repeatable processes to address those problems. Finally, it means you market those solutions internally and show how they bring value to the business. Taking it one step further, it also means that you leverage technology to support these services and continue to develop and improve the services based on feedback.So how does one go about creating these solutions inside a legal team? The first step is all about understanding the needs of the business. You can look internally at the requests the legal department receives to get an understanding of what the business is coming to the legal department for. Next, you want to speak to leaders from different groups in the business to understand what legal needs exist that are not coming into the legal department but should be addressed. Which leaders to speak to will depend a bit on your organization but I would recommend connecting with the following, at minimum: sales, finance, engineering (or product) as well as regional leaders in any key regions. More on this to come in my next blog on interviewing people internally to understand the organization’s needs.Once you have the information, it is helpful to create a list. I like to use the format below:Problems to SolveOnce you have a pretty solid list, you should brainstorm high-level recommended solutions (not the detailed how). This will include things like solving a certain need through documentation (e.g. a “how-to guide” or a template contract). It may include things like facilitating the intake of legal requests or facilitating access to contract information. Once you have your list of potential solutions, there are two next steps. For the set of existing solutions, you should group those into categories and make sure that you are adequately marketing and reporting on those (more on this in a future post). For the set of solutions that are future state, identify how you are going to address this need. When looking at the gaps, I like to categorize the gaps in the following ways so I can understand the budget impact and the division of work.Note that urgency speaks to how quickly the need needs to be solved overall and not necessarily the urgency of a specific request. For example, it speaks to how urgently people need a contract database as opposed to how quickly someone needs information about a specific contract. Pervasiveness addresses how many internal departments/employees have this need. Is it centered around just a small group within one department or is it a need expressed by multiple departments? The relationship to the company strategy should be focused on how much this need moves the business forward. Does it facilitate the company’s #1 strategy? When you complete this list, I recommend grouping it into like needs. If there are overlapping needs, you may want to create a consolidated item but make sure you capture the pervasiveness of it.Recommendations for Filling The GapsBy going through the above process you will have a good understanding of the various needs and solutions in your organization. In the next blog in the series, I will overview how to interview people internally to understand the organization’s needs.legal-operationslegal-ops, blog, legal-operations,legal-ops; bloglighthouse

Discovery in litigation or investigations invariably leads to concerns over protection of privileged information. With today’s often massive data volumes, locating email and documents that may contain legal advice or other confidential information between an attorney and client that may be privileged can be a needles-in-a-haystack exercise. There is little choice but to put forth best efforts to find the needles.Whether it’s dealing with thousands (or millions) of documents, emails, and other forms of electronically stored information, or just a scant few, identifying privileged information and creating a sufficient privilege log can be a challenge. Getting it right can be a headache — and an expensive one at that. But get it wrong and your side can face time-consuming and costly motion practice that doesn’t leave you on the winning end. A look at past mishaps may be enlightening as a reminder that this is a nuanced process that deserves serious attention.Which Entity Is Entitled to Privilege Protection?A strong grasp of what constitutes privileged content in a matter is important. Just as important? Knowing who the client is. It may seem obvious, but history suggests that sometimes it is not; especially when an in-house legal department or multiple entities are involved.Consider the case of Estate of Paterno v. NCAA. The court rejected Penn State’s claim of privilege over documents Louis Freeh’s law firm generated during an internal investigation. Why? Because Penn State wasn’t the firm’s client. The firm’s engagement letter said it was retained to represent the Special Investigations Task Force that Penn State formed after Pennsylvania charged Jerry Sandusky with several sex offenses. There was a distinct difference between the university and its task force.The lesson? Don’t overlook the most fundamental question of who the client is when considering privilege.The Trap of Over-Designating DocumentsWhat kind of content involving the defined client(s) is privileged? This can be a tough question. You can’t claim privilege for every email or document a lawyer’s name is on, especially for in-house counsel who usually serve both a business and legal function. A communication must be confidential and relate to legal advice in order to be considered privileged.Take Anderson v. Trustees of Dartmouth College as an example. In this matter, a student was expelled in a disciplinary action filed suit against Dartmouth. Dissatisfied with what discovery revealed, the student (representing himself) filed a motion to compel, asking the judge to conduct an in camera review of what Dartmouth had claimed was privileged information. The court found that much of the information being withheld for privilege did not, in fact, constitute legal advice, and that Dartmouth’s privilege claim exceeded privilege’s intended purpose.Dartmouth made a few other unfortunate mistakes. It labeled entire email threads privileged instead of redacting the specific parts identified as privileged. It also labeled every forwarded or cc’ed email the in-house counsel’s name was on as privileged without proving the attorney was acting as a legal advisor. To be sure, the ability to identify only the potentially privileged parts of an email thread — which could include any number of direct, forwarded and cc’d recipients and an abundance of inclusive yet non-privileged content — is not an easy task, but unfortunately, it is a necessary one.The lesson? If the goal of discovery is to get to the truth — foundational in American jurisprudence — courts are likely to construe privilege somewhat narrowly and allow more rather than fewer documents to see the light of day. In-house legal departments must be especially careful in their designations given the flow and volume of communications related to both business and legal matters, and sometimes the distinction is difficult to make.Be Ready to Back Up Your ClaimNo matter how careful you are during the discovery process, the other party might challenge your claim of privilege on some documents. “Because we said so” (aka ipse dixit) is not a compelling argument. In LPD New York, LLC v. adidas America, Inc, adidas claimed certain documents were privileged. When challenged, adidas’ response was to say LPD’s position wasn’t supported by law. The court said: Not good enough. Adidas had the burden to prove the attorney-client privilege applied and respond to LPD’s position in a meaningful way.The lesson? For businesses, be prepared to back up a privilege claim that an in-house lawyer was acting in their capacity as a legal advisor before claiming privilege.A Protection Not Used Often Enough: Rule 502(d)Mistakes do happen, however, and sometimes the other party receives information they shouldn’t through inadvertent disclosure. With the added protection of a FRE 502(d) order, legal teams are in a strong position to protect privileged information and will be in good shape to get that information back. Former United States Magistrate Judge Andrew Peck, renowned in eDiscovery circles, is a well-known advocate of this order.The rule says, “A federal court may order that the privilege or protection is not waived by disclosure connected with the litigation pending before the court — in which event the disclosure is also not a waiver in any other federal or state proceeding.”Without a 502(d) order in place, a mistake usually means having to go back and forth with your opponent, arguing the elements under 502(b). If you’re trying to claw back information, you have to spend time and money proving the disclosure was inadvertent, that you took reasonable steps to prevent disclosure, and you promptly took steps to rectify the error. It’s an argument you might not win.Apple Inc. v. Qualcomm Incorporated is a good example. In 2018, Apple lost its attempt to claw back certain documents it mistakenly handed over to Qualcomm during discovery in a patent lawsuit. The judge found Apple didn’t meet the requirements of 502(b). Had Apple established a 502(d) order to begin with, 502(b) might not have come into play at all.The lesson? Consider Judge Peck’s admonition and get a 502(b) order to provide protection against an inadvertent privilege waiver.Advances in Privilege IdentificationLuckily, gone are the days where millions of documents have to be reviewed by hand (or eyes) alone. Technological tools and machine learning algorithms can take carefully constructed privilege instructions and find potentially privileged information with a high degree of accuracy, reducing the effort that lawyers must expend to make final privilege calls.Although automation doesn’t completely take away the need for eyes on the review process, the benefits of machine learning and advanced technology tools are invaluable during a high-stakes process that needs timely, accurate results. Buyer beware however, such methods require expertise to implement and rigorous attention to quality control and testing. When you’re able to accurately identify privileged information while reducing the stress of creating a privilege log that will hold up in court, you lessen the risk of a challenge. And if a challenge should come, you have the data to back up your claims.ai-and-analyticsprivilege, ai-big-data, blog, ai-and-analytics,privilege; ai-big-data; bloglighthouse

A good discovery strategy goes beyond complying with production obligations. When preparing for discovery matters, law firms and legal corporate departments most often focus on developing a compliant and cost-effective responsive review capability with the appropriate expert personnel, technology, and workflows. After all, once in place, a tested responsive review capability can provide legal counsel the cost predictability, security, and control needed to focus on legal strategy early on in large litigation matters.Yet, while it is necessary to develop a reliable in-house or managed responsive review capability for document-intensive litigation, being ready for the demands of modern day discovery extends beyond complying with production obligations. Most notably, the work of fact-finding often continues well after production, and introduces its own unique complexities and opportunities requiring special tactical attention.Responsive review and finding key documents require different workflows. There is a substantive difference between identifying what is responsive for production versus honing in on the key information that ultimately decides a case or investigation. While responsive review focuses on compliance with negotiated and documented requests for production, targeted fact-finding focuses on legal hypothesis-testing and story development in an actively changing and open-ended context.As such, the mix of skills, technologies, and workflows required for responsive review does not necessarily extend beyond the production phase. Honing in on key documents and dispositive information buried in large data sets requires tactical agility and adaptation that efficient responsive review approaches limit by design in order to achieve production compliance at scale.Targeted fact-finding requires an iterative process and an expert team.A common need shared across fact-finding case teams is quick identification of key documents to help develop a robust and coherent fact-based narrative. Rather than casting a broad net to look for similarities among documents as you would do in a responsive review, fact-finding teams require an ability to sleuth through large data sets in order to identify key players, reveal hidden connections between them, and establish an overall picture and timeline of what happened.More specifically, rather than leveraging linear review workflows—even those optimized by technology-assisted review (TAR)—targeted fact-finding is best supported by iterative workflows in which attorneys and discovery experts deeply familiar with the case conduct tailored interrogations of the data to find the information that will best help with developing the case team’s understanding of the matter.Broaden the notion of discovery for better preparedness. Limiting discovery preparedness to achieving scalable responsive review gives short-shrift to developing a capacity for targeted fact-finding. Broadening the notion of discovery preparedness to include fact-finding means reassessing your discovery capabilities beyond production-oriented questions, such as what number of documents will require eyes-on review or what level of accuracy can be achieved. It makes sense to make more qualitative, legal expert-based assessments such as: Have we been able to uncover the full extent of the relevant fact pattern? How confident are we that we have connected the dots regarding who acted improperly and who had knowledge of it?To answer these sorts of questions, senior members of the litigation team need to be actively informed at a granular level regarding fact-finding approaches and outcomes. A robust and effective fact-finding function can provide a critical advantage in not only witness and trial preparation, but also in early case assessment, internal investigations, and government subpoenas, increasingly important discovery contexts to consider in light of increased regulatory, shareholder, and public scrutiny of corporate fraud and wrong-doing.For more on fact-finding, see: eDiscovery for Investigations: Different Goal, Different Approach. ediscovery-reviewblog, ediscovery-reviewbloglighthouse

In a world of ever-increasing and evolving self-service, spectra models (think Amazon Go, fast food self-order kiosks, or even the self-service, spectra check in and check out at hotels), it’s no wonder the eDiscovery industry is headed in the same direction. In the last few years, new and improved SaaS eDiscovery tools have exploded onto the scene as corporations and law firms have started to embrace a self-service, spectra approach for executing the discovery work associated with both internal investigations and proper legal matters.As the historically risk-averse legal world has been slow to get on board with self-service, spectra eDiscovery models, you’re likely to still encounter objections if you ask your team to take the leap and invest in a self-service, spectra eDiscovery tool. Below, I outline the common objections I have encountered to self-service, spectra models and how you can overcome them by sharing some key value differentiators of on-demand eDiscovery tools that should persuade your team to embrace these new offerings and leave the antiquated, expensive on-prem solutions for good.Data Security Risks – The first, and potentially biggest, objection to the adoption of a self-service, spectra program often centers on concerns over data security and the associated risk. Corporations and law firms alike are concerned that self-service, spectra means cloud-based and as a result a lack of security and an increased exposure to risk. Historically, companies have been more comfortable with on-prem eDiscovery solutions because they have viewed that as meaning that they had complete control of their data without having to rely on a vendor or worry about their data being commingled with other clients’ data.However, with that control comes with a lot of risk, which can be greatly minimized by having a SaaS vendor that offers a private cloud. This private cloud can be a perfect solution because they do not have the same security concerns that are involved with the public cloud. When vetting potential SaaS partners, make sure to look for those that carry SOC 2, ISO, and HIPAA security certifications to ensure that your providers are staying up to speed on the latest security requirements.Steep Learning Curves – Oftentimes people I chat with will associate self-service, spectra with steep learning curves and lots of tedious training. This isn’t always the case.Although this may be true with some self-service, spectra solutions, but it’s important to note that not all platforms are created equal. Some solutions provide extensive functionality with a complex, hard to use, difficult to understand interface, while others strive to provide a simple interface often at the expense of functionality, while a rare few bridge the two finding that perfect blend of robust functionality and ease of use. Start the assessment into usability by understanding the true training required for the solutions you are evaluating, weigh the functionality vs. your team’s needs and skillset, talk to other users who have already adopted the solutions to validate the actual training lift, and finally showcase those findings with your team.Lack of Support – A common objection to self-service, spectra solutions is that they lack on-demand support and access to additional training and help if needed. Today’s lawyers and eDiscovery managers need to move through matters quickly and efficiently, and without access to readily-available help it frequently stalls the matter’s progress significantly. When looking into self-service, spectra solutions, choose one that offers on-demand support when, where, and how you need it. There are companies out there that offer this blend of autonomous control and augmented support and it is an easy objection to overcome if you can showcase that to your team. Minimal Flexibility – Frequently there is a hesitation to move forward with self-service, spectra due to the fear of getting stuck managing a matter in a self-service, spectra tool that could become large and/or complex and require additional help/support outside of your team’s capabilities or availability.Like I mentioned above, select a tool that also offers expert support. You can ease your team members’ worries by sharing that some self-service, spectra tools offer the ability to move from self-service, spectra to full service support when needed and scale up or down quickly.Too Pricey – The last major objection that I want to address is around the fact that many still believe self-service, spectra tools are too pricey and do not offer the ROI they need to see in order to make the switch.However, in an on-prem, behind the firewall model, there often are large up-front costs to purchase and install the technology as well as continued maintenance costs during the life of the product. In addition to the hardware costs there is the increased headcount necessary to support these platforms 24x7. With SaaS, the vendor purchases and maintains the software, as well as manages ongoing costs like upgrades and licensing. Managing an IT infrastructure and maintaining servers for eDiscovery data is a big cost for law firms and corporations often with no cost recovery mechanism. This cost burden can be greatly minimized with a SaaS solution.These top objections often come up in conversations around the adoption of a new self-service, spectra solution and I hope this blog has better prepared you to address them as you work to get your team on board. Feel free to reach out to further discuss these or other objections you may be facing at bthompson@lighthouseglobal.com.ediscovery-reviewcloud, self-service, spectra, blog, ediscovery-review,cloud; self-service, spectra; blogbrooks thompson

In this two-part series, I interview Gordon J. Calhoun, Esq. of Lewis Brisbois Bisgaard & Smith LLP about his thoughts on the state of eDiscovery within law firms today, including lessons learned and best practices to help attorneys overcome their trepidation of electronic discovery and build a better litigation practice. This second blog focuses on how attorneys within law firms can save their clients money, achieve better outcomes, and gain more repeat business once they overcome common misconceptions around eDiscovery.You mentioned earlier that you think attorneys who try to shoehorn volumes of electronic data into older workflows developed for paper discovery will likely cause attorneys to lose clients. Can you explain how? Sure. My point was that non-technological workflows often pop into the minds of attorneys because they are familiar, comfortable approaches to responding to document requests. Because they are familiar and can be initiated immediately, there is a great temptation to jump in and avoid planning and employing the expertise essential for an optimal workflow. Unfortunately, jumping in without much planning produces a result that is often unnecessarily costly to clients, particularly if the attorneys employ in-house resources (which are usually several times more costly than outsourced staff). In-house resources often regard document review and analysis as an undesirable assignment and have competing demands for their time from other projects and cases. This can result in unexpected delays in project completion and poor work product (in part because quality degrades when people are required to perform tasks they dislike). The end result is untimely, lower quality, and more costly than anticipated, which will ultimately cost the attorney their client.Clients will always gravitate towards the professional who can deliver a better, more cost-effective, and more efficient solution while avoiding motion expenses. That means that attorneys who are informed enough to use technology to save clients money on multiple cases are going to earn the trust and confidence of more and more clients. And that is the answer to the question as to what’s in it for the professional if he or she takes the time to learn about or partners with someone who already knows eDiscovery.Well, coming from a legal technology company, I agree with that sentiment. But we also tend to see attorneys from the other end of the spectrum: lawyers who understand the benefits advanced eDiscovery technology can provide, but avoid it because of fears around overhead expense and surprise fees. Have you seen this within your own practice? If so, how do you advise attorneys who may have similar feelings? I experience the same thing and, again, this type of thought process is completely understandable. When eDiscovery technologies were comparatively new, they seemed disproportionately expensive. The cost to process a GB of data could exceed $1,000, hosting charges ran into the many tens of dollars per month and there were no analytics to expedite review. When the project management service was in its infancy, too many of those providing services simply followed uninformed instructions from counsel. An instruction to process data was not met with inquiries as to whether all data collected should be processed or if an alternative should be explored when initial analysis indicated the data expansion would be unexpectedly large. Further, early case assessment (ECA) strategies utilizing only extracted text and metadata were years in the future. The only saving grace was that data volumes were miniscule compared to what they are today. But that was not enough to prevent widespread reports about massive eDiscovery vendor bills. As you might suspect, the problem was not so much the technology or even the lack thereof as it was the failure to spend the time to develop an appropriate workflow and manage the eDiscovery process so the results were cost effective. Any tips on how attorneys can overcome the remnant fear of eDiscovery “sticker shock”?This challenge can be met by research, planning, and negotiation: research into the optimal technologies and which providers are equipped to provide them, planning an appropriate workflow, and negotiation with eDiscovery platform providers to customize the offerings to the needs of your case. If you have the aptitude, consider investing some time and doing some research about eDiscovery solutions that provide predictable, transparent prices outside of the typical hourly and per-GB fee structure. A good eDiscovery platform provider should work with you to develop a fee arrangement that makes sense for your caseload and budget. There is no reason why even a small firm or individual practitioner cannot negotiate subscription-based or consumption-based fees for eDiscovery solutions the same way that forward thinking serial litigants like large corporations and insurers have. The pricing models exist and there is no reason they cannot be scaled for users with smaller demands. Under this type of arrangement, there will be no additional costs or surprise fees, which in turn will allow any practitioner to pass that price predictability on to his or her clients. Ultimately, this lower cost, increased predictability, and efficiency will enable an attorney to grow his or her book of business with repeat customers and referrals.So, if an attorney is able to negotiate an alternative fee arrangement with a legal technology provider, is that the end of the problem? Should that solve all an attorney’s eDiscovery concerns? It’s a start – but no. Even with a customized eDiscovery technology solution, part of the concern for most attorneys is the magnitude of the effort required to respond to discovery requests. On one hand, they’re faced with document requests fashioned by opposing counsel fearful of missing something that might be important unless they are massively overinclusive. They ask for each, every, and all documents and any form of electronic media that involves, concerns, or otherwise relates to 30, 50, 100, or more discrete topics. On the other hand, the attorney must reconcile this task of preserving, identifying, collecting, processing, analyzing, reviewing and producing ESI in a manner that complies with the applicable discovery laws or case specific discovery orders… all under what may be a modest budget approved by the client. This is where experience (or guidance from an experienced attorney), as well as a good eDiscovery technology provider can be a huge help. The principle that underlies a solution to the conundrum as to how to manage an overly broad discovery request with a limited budget is: proportionality. Emphasizing this principle is a major focus of the 2015 amendments to the FRCP. Got it. I think the logical follow up question to that answer is: how can attorneys attain “proportionality” in the face of ridiculous discovery requests (while also not exceeding the limited amount the client is prepared to spend)?The key to balancing these conflicting demands is insisting upon proportionality early and often. The principle needs to be addressed at a granular level with a robust understanding of the client’s data that will be the subject of opposing counsel’s discovery requests. For example, the number of custodians from whom data should be collected should not be a laundry list of everyone who might have knowledge about the issues in the case. Rather, counsel should be focused on the key players and how much data each has. The volume of data that counsel can afford to collect, process, analyze, review, and produce should depend largely on what the litigation budget is, which in turn should generally depend on the amount in controversy. There are exceptions to this rule of thumb, but this approach to proportionality needs to be raised during the initial meetings of counsel in advance of the first case management order. If the case is one where the general rule does not apply (e.g., a matter of public interest), the client should be informed immediately because the cost of litigation is likely to be disproportionate to its economic value and the client may prefer to have some other entity litigate the issue. An experienced attorney should be involved in this meet and confer process because the results of these early efforts are likely to create the foundation and guard rails for the remainder of the case. Any issues that are left to future negotiation create a potential for costs to balloon in unexpected ways. Can you dive a bit deeper into proportionality at different phases of the discovery process? Is there anything else attorneys can do to keep cost from ballooning before data is collected?As I alluded to a moment ago, one key to controlling scope and cost is to negotiate a limited number of custodians that is proportional to the value of the case. In larger cases, it will be appropriate to create tiers of custodians and limit progression into the lower tier custodians to those instances where opposing counsel make a good faith showing that additional discovery is necessary based on identifiable gaps of information rather than upon speculation about what might be found if more discovery is permitted. If opposing counsel doesn’t agree to a limited number of custodians or staging discovery in larger cases, counsel would be well advised to prepare a case management order or a protective order to keep the scope of discovery proportional to the value of the case. To be successful, an attorney and his or her technology provider will have to understand the data in the client’s possession and provide metrics and costs associated with the alternative approaches to discovery.Great advice. How about once data is collected and analysis has begun? How can attorneys keep costs within budget once they've got the data into an eDiscovery platform?Attorneys should continue to budget proportionally throughout the case. This budget will obviously include the activities identified by the Electronic Discovery Reference Model (EDRM). The EDRM provides a roadmap to respond to opposing parties’ discovery requests: identifying those documents that are needed to make our case, regardless of whether opposing parties requested them; winnowing the documents identified to a subset for use in deposition preparation; drafting potentially dispositive motions; and preparing for mediation; and, if necessary, preparing for inclusion on the trial exhibit list. The EDRM was designed to help attorneys identify documents that are reasonably calculated to lead to the discovery of admissible evidence or relate to claims and defenses asserted in the case. In a case with 100,000 documents collected, that could easily be 10,000 to 15,000 documents. The documents considered for use in depositions, law and motion, or mediation will be a small fraction of that amount and will include a similar culling of those documents produced by other parties and third parties. Only a fraction of those will make it onto the trial exhibit list and fewer will be presented to the trier of fact.Responding to discovery and preparing the case for resolution are two very different tasks and the attorney’s budget must accommodate these two different activities. Monies must be reserved for other written discovery requests, both propounding them and responding to them, and for depositions. Because the per-GB prices for these activities are predictable, an attorney and technology provider should be able to readily determine how much information they can afford to collect and put into the eDiscovery workflow. Counsel needs to be ready to share this information with opposing parties during the early meetings of counsel. But what happens when there is just a legitimately large amount of data, even after applying all the proportionality tactics you described earlier? Counsel should only agree to look at more data than that to which the parties originally agreed if opposing counsel can show good cause to incur that time and expense. If more data needs to be analyzed, the only reliable way to avoid busting the budget is to use AI to build on the document classification that occurred during the initial round of eDiscovery activities. Counsel should take advantage of statistically defensible sampling to determine the prevalence of responsive documents in the data and cut off analysis and review when a defensible rate of recall has been achieved. The same technologies should be employed to identify documents that should not be produced, e.g., those that are privileged or contain trade secrets unrelated to the pending litigation or other data exempt from discovery – enabling counsel to reduce the amount of expensive attorney review required on a given case.By proactively managing eDiscovery proportionality and leveraging all the efficiency that modern eDiscovery platforms provide (either by developing the necessary expertise to do so or associating with an attorney who does) – any lawyer will be able to handle any discovery request in a cost-effective manner.You mentioned choosing a database and legal technology provider. Do you have any advice for attorneys on how to choose the best one to meet their needs?I won’t weigh in on specifics, but I will say this: do the necessary research or consult with someone who has. In addition to investigating the various technologies available, counsel must become familiar with a variety of pricing models for delivery of the technologies needed to respond to eDiscovery requests. Instead of treating every case as an a la carte proposition, consider moving to a subscription-based self-service eDiscovery platform solution. This allows counsel savvy with the technology to control his or her cases within the platform and manage costs in a much more granular way than is possible when using a full-service eDiscovery technology provider, without incurring additional licensing, hosting, and technology fees. With a self-service solution, a provider hosts the data within their own cloud (and thus takes on the data security, hosting, and technology fees), while counsels gain access to all the current versions of eDiscovery tools to help manage the client’s costs. It will also allow counsel to customize the platform and automate workflows to meet his or her own specific needs, so that no one is spending time and money re-inventing the wheel with every new case. A self-service solution also comes with the added benefit of being immediately available from any web browser and gives counsel the ability to transfer data into platform at the touch of a button. (This means that when a prospective client asks whether you have a solution to handle the eDiscovery component of a case, the answer will always be an immediate “yes”).What happens if counsel does not feel ready to take on all eDiscovery responsibilities in a “self-service” model?If counsel is not ready to take on full responsibility for managing the eDiscovery process but still wants the cost-savings of a self-service model, find a technology provider that offers project management services and guidance that will act as training wheels until counsel is ready to navigate the process without assistance. There are also service providers who offer flexible arrangements, where large matters can be handled by their full-service team while smaller matters or investigations can remain “self-service” and be handled directly by counsel.Those are great tips, Gordon – I couldn’t have said it better myself. Any last thoughts for attorneys related to discovery and leveraging eDiscovery technology? Thank you, it’s been a pleasure. As for last thoughts, I think it would be this: in 2021, no attorney should fear responding to eDiscovery requests. Attorneys who still have that fear need to start asking, “If the data exists electronically, can I use technology to extract what I need less expensively than if I put eyeballs on every document?” The answer is almost always, “Yes.” The next question those attorneys should ask is, “How do I go about extracting the information I need at the lowest possible cost?” The answer to that question may be unique to each attorney, and this is where I recommend doing some up-front research and preparation to identify the best technology solution before you are looking down the barrel at a tight discovery deadline.Ultimately, finding the right technology solution will enable you to meet every discovery request with confidence and ultimately grow your book of business. If you would like to discuss this topic further, please reach out to Casey at cvanveen@lighthouseglobal.com and/or Gordon Calhoun at Gordon.Calhoun@lewisbrisbois.com.ediscovery-review; ai-and-analyticsediscovery-process, blog, spectra, law-firm, ediscovery-review, ai-and-analyticsediscovery-process; blog; spectra; law-firmcasey van veen

Lighthouse Microsoft 365 (M365) experts, John Holliday and John Collins, recently teamed up with Reed Smith to present the M365 in 5 Foundation Series on Reed Smith’s Tech Law Talks podcast. The series dives into operational considerations when rolling out M365 tools related to governance, retention, eDiscovery, and data security across a broad range of applications, from Exchange and SharePoint to all things Microsoft Teams.Check out the lineup below and click the titles of each podcast to give them a listen.M365 in 5 – Part 1: Exchange Online – Not just a mailboxDiscover the enhanced functionality of EXO, including new data types and the potential for enhanced governance.M365 in 5 – Part 2: SharePoint Online – The new file-share environmentHear about the enhanced file share and collaboration functionality in SharePoint Online, including real-time collaboration, access controls, and opportunities to control retention and deletion.M365 in 5 – Part 3: OneDrive for Business – Protected personal collaborationLearn about OneDrive for Business and how organizations can use it for personal document storage, such as giving other users access to individual documents within an individual’s OneDrive and acting as the storage location for all Teams Chats.M365 in 5 – Part 4: Teams – An introduction to collaborationListen to an introduction to Teams and how it is transforming the way organizations are working and communicating.M365 in 5 – Part 5: Teams Chats – Modern communicationsUncover the enhanced functionality of M365’s new instant messaging platform, including persistent chats, modern attachments, expressive features, and priority messaging, which enhance communication but can bring increased eDiscovery or regulatory risks.M365 in 5 – Part 6: Teams Channels – The virtual collaboration workspaceHear how Teams Channels are changing not only the way organizations work and collaborate, but also key legal and risk considerations that should be contemplated.M365 in 5 – Part 7: Teams Audio/Video (A/V) ConferencingDive into the functionality and controls of audio/video conferencing capabilities, including the integration of chats, whiteboards, translation, and transcription services.The Tech Law Talks podcast hosts regular discussions about the legal and business issues around data protection, privacy and security; data risk management; intellectual property; social media; and other types of information technology. For more information regarding the show, follow the link here: https://reedsmithtech.podbean.com.If you have questions about how to develop and maintain legal and compliance programs around M365, reach out to us at info@lighthouseglobal.com.microsoft-365; information-governancemicrosoft, blog, microsoft-365, information-governancemicrosoft; bloglighthouse

In case the complexities of corporate data weren’t creating enough turbulence to keep corporate and legal teams up at night, along comes a prolonged pandemic to really shake things up. Because now, a complex data landscape has also become a complex employee landscape.What has been dubbed the “great resignation” (approximately 38 million workers voluntarily quit their jobs in 2021) has left many companies shaken as they struggle to adapt their organizations to a reconfigured and remote workforce. With little time to plan for the risks and contingencies such a seismic shift would normally entail, companies are now playing catch-up, seeking ways to ensure proper data management, better responses to fast-moving litigation and internal investigations, and enhanced security as they grapple with offsite employees, transformative applications, and the impact of an exodus that may have caused company data to escape its bounds.These unique circumstances present a number of challenges for companies and their legal teams alike. In a webinar with Today’s General Counsel, I was pleased to join Scott McVeigh, industry principal from Onna, to discuss the ways in which many companies have been affected. We looked at the recent workplace disruption and considered the impact: What data risks have emerged or intensified? What efficiencies or advantages? What areas of the company data environment deserve renewed focus? What steps can internal teams take to help ensure that data concerns are addressed and legal imperatives met? A Shift to Remote Work Accelerates Transition to the CloudPrior to the pandemic, an estimated 20% of the U.S workforce was working remotely. By December, 2020, that number had increased to 71%. Even with offices now deemed safer as the pandemic wanes, it is anticipated that more than 51% of the U.S. workforce will continue to be remote or hybrid.The impact of this shift has already been profound, reshaping the use, format, and storage of data. As many as 81% of organizations say the pandemic accelerated their cloud timelines as they raced to engage with new tools and applications that flooded the market to accommodate the remote workforce. Online collaboration has now become the new normal, with document sharing apps, chat functionalities, and web conferencing becoming the dominant forces that underpin daily work. Enhanced Collaboration — A Mixed Blessing While this shift may have resulted in some efficiencies as more informal practices took hold, the explosion of collaborative data technologies has also created significant challenges, especially for data and records management, security, and legal teams. As a result, some important enterprise areas are ripe for renewed attention and innovation:Information governance models: The disrupted workforce has made information governance efforts more complicated—and more necessary. Remote collaboration and sharing applications mean more data in more places, making it harder for internal teams to create and maintain a cohesive vision of the data landscape to contain and control growing data volumes.Rapid data growth from both authorized and unauthorized tools and new forms of communication (think gifs, memes, and emojis) makes it easier for data to proliferate, morph, even disappear, which may call for modified or additional policies and procedures. From a data security standpoint, privacy breaches coupled with other security stressors are magnified as siloed data, a perennial problem, pressure-tests existing processes and policies.eDiscovery and preservation imperatives: In the implementation of cloud applications, preserving and collecting data in a defensible manner has not been a top priority. More tools enabling informal, dispersed, and fluid content challenge the paradigm of traditional collection and review. Where is a particular kind of data living and who controls it? Who is the custodian or author of content in shared collaborative spaces? With so many new data types, what is now the definition of a “document” or a conversation?Employee transitioning: As employees moved offsite or departed during the pandemic, company data may have gone with them — if not through malicious exfiltration, then just because HR and IT, with reduced teams as well, could not keep up with the onboarding and offboarding process. One top concern for organizations is that the lost data or IP could have gone to a competitor. Training requirements: With workers at a distance, training on company privacy, security, and preservation policies — which should be intensifying — may be taking a back seat to other business priorities impacted by the pandemic. Too, cultivating a data-sensitive culture is now more difficult with employees often untethered from the norms of company data access and storage and little to no face-to-face interaction with other employees and their own managers. Law Firms and Legal Departments Not Exempt from DisruptionTo complicate matters, as companies were transformed by the pandemic, so too were the law firms and legal departments that support them. Already in a state of flux, the legal market was highly impacted by both employee departures and the migration to remote work, relatively foreign to an entrenched in-office culture. Lack of attention to document management, often a law firm weakness, has just added fuel to the fire.The resignation-induced talent drain has likely affected workflows, adding to inefficiencies and duplicative work as corporate and legal knowledge, both in-house and outside, dissipated with the overall disruption of formerly routine processes and responsibilities. It has certainly impacted eDiscovery processes; legal professionals are still working to master the art of conducting discovery remotely from cloud-based data sources.Bucking the Trends: Take These Steps to Reduce RiskThe disrupted workplace calls for renewed diligence, nimbleness, and a certain amount of creativity on the part of internal teams responsible for data and its management. Most of all, it requires rigorous attention to potential risks exacerbated by a still-evolving landscape.Here are some important steps companies can take to reduce risk: Scrutinize what may now be a very different data landscape. As in pre-pandemic times, knowing where data resides and in what format is a big part of the battle. With new tools and cloud storage locations making everything even more complex, thinking through applications and the data they generate before they roll out can save time, effort, and grief down the line. Analyze: Who uses what applications? Where does the data go and how is it stored? Who has control over it? From an eDiscovery standpoint, with so much data in play, it pays to scale efforts to potential returns; focusing on the most-used data sources is more fruitful than “boiling the ocean.” Cultivate stakeholder partnerships. As the workforce transforms, partnerships among internal stakeholders, especially IT, compliance, data privacy, records management, and information security teams — in close coordination with business units — are more important than ever in controlling how and by whom data is created and used. Corporate silos only enhance risk, especially when workers are remote and unsanctioned applications may be proliferating. Remember, though, that data initiatives are most effective when they come from the top, especially if funding is required. Engage the C-suite as much as possible. Improve information governance capabilities. As data pools from multiple collaborative sources and cloud applications proliferate, making prior linear processes cumbersome and expensive, a shift in focus to the left side of the Electronic Discovery Reference Model (EDRM) makes even more sense now. With the right cloud-based tools and services, as well as good information governance models, teams can perform better upstream and reduce downstream costs.Foster a culture of data awareness and protection. Training, training, training — for both current and incoming employees — is critical. Sound policies mean nothing if employees are unaware of or don’t abide by them or don’t understand the nature of the risk they are meant to address. Educate employees on data “ownership” best practices. Encourage sound data hygiene and enhance onboarding and offboarding procedures to take data risks into account, especially those related to preservation imperatives. Remember that inbound data from new employees that works its way into the company can be just as problematic as data exfiltration. Review and, if necessary, update records management policies. Records management policies should be considered programmatically to align with the nature of the business. Reducing company exposure by updating policy gaps that may be caused by evolving privacy regulations (e.g., GDPR, CCPA/CPRA, etc.) should be a top priority for any company’s records and data management teams. Remember that training goes hand in hand with any policy changes.Engage experts where you need them. Data complexities of today, especially related to privacy and security, may require the expertise beyond that routinely found in-house. Be sure to work with providers and experts well-versed in today’s challenges.Leverage technology where possible, with expertise in mind. Various data automation tools can provide the power to import, manage, and modify records in ways never before possible. AI and categorization tools can be used to assess data in place, potentially mitigating the need for linear collection, processing, and review of data in discovery. Automated tools can enable a more managed examination of departing employee data. But technology not carefully deployed or without the right experts behind the scenes can diminish the potential benefits. Know what questions to ask. Be an informed and thoughtful user: implement wisely. If you are interested in this topic, feel free to reach out to me at dblack@lighthouseglobal.com. chat-and-collaboration-data; forensics; information-governanceemerging-data-sources, cloud-security, red-flag-reporting, departing-onboarding-employee, pii, blog, record-management, risk-management, chat-and-collaboration-data, forensics, information-governance,emerging-data-sources; cloud-security; red-flag-reporting; departing-onboarding-employee; pii; blog; record-management; risk-managementdaniel black

While the U.S. is figuring out privacy laws at the state and federal level, artificial and augmented intelligence (AI) is evolving and becoming commonplace for businesses and consumers. These technologies are driving new privacy concerns. Years ago, consumers feared a stolen Social Security number. Now, organizations can uncover political views, purchasing habits, and much more. The repercussions of data are broader and deeper than ever.Lighthouse (formerly H5) convened a panel of experts to discuss these emerging issues and ways leaders can tackle their most urgent privacy challenges in the webinar, “Everything Personal: AI and Privacy.”The panel featured Nia M. Jenkins, Senior Associate General Counsel, Data, Technology, Digital Health & Cybersecurity at Optum (UnitedHealth Group); Kimberly Pack, Associate General Counsel, Compliance, at Anheuser-Busch; Jennifer Beckage, Managing Director at Beckage; and Eric Pender, Senior Director at Lighthouse (formerly with H5); and was moderated by Sheila Mackay, Managing Director at Lighthouse (formerly with H5).While the regulatory and technology landscape continues to rapidly change, the panel highlighted some key takeaways and solutions to protect and manage sensitive data leaders should consider:Build, nurture, and utilize cross-functional teams to tackle data challengesDevelop robust and well-defined workflows to work with AI technology Understand the type and quality of data your organization collects and stores Engage with experts and thought leadership to stay current with evolving technology and regulations Collaborate with experts across your organization to learn the needs of different functions and business units and how they can deploy AI Enable your company’s innovation and growth by understanding the data, technology, and risks involved with new AIDevelop collaboration, knowledge, and cross-functional teamsWhile addressing challenges related to data and privacy certainly requires technical and legal expertise, the need for strong teamwork and knowledge sharing should not be overlooked. Nia Jenkins said her organization utilizes cross-functional teams, which can pull together privacy, governance, compliance, security, and other subject matter experts to gain a “line of sight into the data that’s coming in and going out of the organization.”“We also have an infrastructure where people are able to reach out to us to request access to certain data pools,” Jenkins said. “With that team, we are able to think through, is it appropriate to let that team use the data for their intended purpose or use?”In addition to collaboration, well-developed workflows are paramount too. Kimberly Pack explained that her company does have a formalized team that comes together on a bi-monthly basis and defined workflows that are improving daily. She emphasized that it all begins with “having clarity about how business gets done.”Jennifer Beckage highlighted the need for an organization to develop a plan, build a strong team, and understand the type and quality of the data it collects before adopting AI. Businesses have to address data retention, cybersecurity, intellectual property, and many other potential risks before taking full advantage of AI technology.Engage with internal and external experts to understand changing regulations Keeping up with a dynamic regulatory landscape requires expanding your information network. Pack was frank that it’s too much for one person to learn themselves. She relies on following law firms, becoming involved in professional organizations and forums, and connecting with privacy professionals on LinkedIn. As she continually educates herself, she creates training for various teams at her organization, including human resources, procurement, and marketing.“Really cascade that information,” said Pack. “Really try to tailor the training so that it makes sense for people. Also, try to have tools and infographics, so people can use it, pass it along. Record all your trainings because everyone’s not going to show up.”The panel discussed how their companies are using AI and whether there’s any resistance. Pack noted her organization has carefully taken advantage of AI for HR, marketing, enterprise tools, and training. She noted that providing your teams with information and assistance is key to comfort and adoption.“AI is just a tool, right?” Pack said. “It’s not good, it’s not bad.” The privacy team conducts a privacy impact assessment to understand how the business can use the technology. Then her team places any necessary limitations and builds controls to ensure the team uses the technology ethically. Pack and Jenkins both noted that the companies must proactively address potential bias and not allow automated decision-making.Evaluate the benefits and risks of AI for your organization The panel agreed organizations should adopt AI to remain competitive and meet consumer expectations. Pack pointed out the purpose of AI technology is for it to learn. Businesses adopting it now will see the benefits sooner than those that wait.Eric Pender noted advanced technologies are becoming more common for particular uses: cybersecurity breach response, production of documents, including privilege review and identifying Personally Identifiable Information (PII), and defensible disposal. Many of these tasks have tight timelines and require efficiency and accuracy, which AI provides.The risks of AI depend on the nature of the specific technology, according to Beckage. It’s each organization’s responsibility to perform a risk assessment, determine how to use the technology ethically, and perform audits to ensure the technology is working without unintended consequences.Facilitate innovation and growth It is also important to remember that in-house and outside counsel don’t have to be “dream killers” when it comes to innovation. Lawyers with a good understanding of their company’s data, technology, and ways to mitigate risk can guide their businesses in taking advantage of AI now and years down the road.Pack encouraged compliance professionals to enjoy the problem-solving process. “Continue to know your business. Be in front of what their desires are, what their goals are, what their dreams are, so that you can actively support that,” she said.Pender says companies are shifting from a reactive approach to a proactive approach, and advised that “data that’s been defensively disposed of is not a risk to the company.” Though implementing AI technology is complex and challenging, managing sensitive, personal data is achievable, and the potential benefits are enormous.Jenkins encouraged the “four B’s.” Be aware of the data, be collaborative with your subject matter experts, be willing to learn and ask tough questions of your team, and be open to learning more about the product, what’s happening with your business team, and privacy in an ever-changing landscape.Beckage closed out the webinar by warning organizations not to reinvent the wheel. While it’s risky to copy another organization’s privacy policy word for word, organizations can learn from the people in the privacy space who know what they’re doing well.ai-and-analytics; data-privacyprivilege, cybersecurity, ai-big-data, pii, blog, preservation, ai-and-analytics, data-privacyprivilege; cybersecurity; ai-big-data; pii; blog; preservationlighthouse

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.

Discover how LighthouseIQ applies machine learning to accelerate and improve image-based discovery.

Learn how Lighthouse AI enhances image analysis for construction litigation, risk, and compliance use cases.

Learn how Lighthouse helps legal and IT teams align for stronger Microsoft 365 data protection and governance.

Hear how a global law firm successfully transitioned eDiscovery in-house with Lighthouse support and technology.

See how Lighthouse helps organizations manage shared mailbox risks and compliance challenges during integrations.

See how Lighthouse helps organizations enhance IG posture by cleaning up redundant, obsolete, and trivial data.

Explore Lighthouse’s cutting-edge digital forensics capabilities that uncover critical insights from complex data sources.

Explore IQ Answers—built to uncover insights faster and improve decision-making across legal data.