Lighthouse Blog

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

Recently, I had the opportunity to (virtually) attend the first three days of Legalweek, the premier conference for those in the legal tech industry. Obviously, this year’s event looked much different than past years, both in structure and in content. But as I listened to legal and technology experts talk about the current state of the industry, I was happily surprised that the message conveyed was not one of doom and gloom, as you might expect to hear during a pandemic year. Instead, a more inspiring theme has emerged for our industry - one of hope through innovation.Just as we, as individuals, have learned hard lessons during this unprecedented year and are now looking towards a brighter spring, the legal industry has learned valuable lessons about how to leverage technology and harness innovation to overcome the challenges this year has brought. From working remotely in scenarios that previously would have never seemed possible, to recognizing the vital role diversity plays in the future of our industry – this year has forced legal professionals to adapt quickly, utilize new technology, and listen more to some of our most innovative leaders.Below, I have highlighted the key takeaways from the first three days of Legalweek, as well as how to leverage the lessons learned throughout this year to bring about a brighter future for your organization or law firm.“Human + Machine” not “Human vs. Machine” Almost as soon as artificial intelligence (AI) technology started playing a role within the legal industry, people began debating whether machines could (or should) eventually replace lawyers. This debate often devolves into a simple “which is better: humans or machines” argument. However, if the last year has taught us anything, it is that the answers to social debates often require nuance and introspection, rather than a “hot take.” The truth is that AI can no longer be viewed as some futuristic option that is only utilized in certain types of eDiscovery matters; nor should it be fearfully viewed as having the potential to replace lawyers in some dystopian future. Rather, AI has become essential to the work of attorneys and ultimately will be necessary to help lawyers serve their clients effectively and efficiently.1Data volumes are exponentially growing year after year, so much so that soon, even the smallest internal investigation will involve too much data to be effectively reviewed by human eyes alone. AI and analytics tools are now necessary to prioritize, cull, and categorize data in most litigations for attorneys to efficiently find and review the information they need. Moreover, advancements in AI technology now enable attorneys to quickly identify categories of information that previously required expensive linear review (for example, leveraging AI to identify privilege, protected health information (PHI), or trade secret data).Aside from finding the needle in the haystack (or simply reducing the haystack), these tools can also help attorneys make better, more strategic counseling and business decisions. For example, AI can now be utilized to understand an organization’s entire legal portfolio better, which in turn, allows attorneys to make better scoping and burden arguments as well as craft more informed litigation and compliance strategies.Thus, the age-old debate of which is better (human or machine learning) is actually an outdated one. Instead, the future of the legal industry is one where attorneys and legal professionals harness advanced technology to serve their clients proficiently and effectively.Remote Working and Cloud-Based Tools Are Here to StayOf course, one of the biggest lessons the legal industry learned over the past year is how to effectively work remotely. Almost every organization and law firm across the world was forced to quickly pivot to a more remote workforce – and most have done so successfully, albeit while facing a host of new data challenges related to the move. However, as we approach the second year of the pandemic, it has become clear that many of these changes will not be temporary. In fact, the pandemic appears to have just been an accelerator for trends that were already underway prior to 2020. For example, many organizations were already taking steps to move to a more cloud-based data architecture. The pandemic just forced that transition to happen over a much shorter time frame to facilitate the move to a remote workforce.This means that organizations and law firms must utilize the lessons learned over the last year to remain successful in the future, as well as to overcome the new challenges raised by a more remote, cloud-based work environment. For example, many organizations implemented cloud-based collaboration tools like Zoom, Slack, Microsoft Teams, and Google Workspace to help employees collaborate remotely. However, legal and IT professionals quickly learned that while these types of tools are great for collaboration, many of them are not built with data security, information governance, or legal discovery in mind. The data generated by these tools is much different than traditional e-mail – both in content and in structure. For example, audible conversations that used to happen around the water cooler or in an impromptu in-person meeting are now happening over Zoom or Microsoft Teams, and thus may be potentially discoverable during an investigation or legal dispute. Moreover, the data that is generated by these tools is structured significantly differently than data coming from traditional e-mail (think of chat data, video data, and the dynamic “attachments” created by Teams). Thus, organizations must learn to put rules in place to help govern and manage these data sources from a compliance, data security, and legal perspective, while law firms must continue to learn how to collect, review, and produce this new type of data.It will also be of growing importance in the future to have legal and IT stakeholder collaboration within organizations, so that new tools can be properly vetted and data workflows can be put in place early. Additionally, organizations will need a plan in place to stay ahead of technology changes, especially if moving to a cloud-based environment where updates and changes can roll out weekly. Attorneys should also consider technology training to stay up-to-date and educated on the various technology platforms and tools their company or client uses, so that they may continue to provide effective representation.Information Governance is Essential to a Healthy Data StrategyRelated to the above, another key theme that emerged over the last year is that good information governance is now essential to a healthy company, and that it is equally important for attorneys representing organizations to understand how data is managed within that organization.The explosion of data volumes and sources, as well as the unlimited data storage capacity of the Cloud means that it is essential to have a strong and dynamic information governance strategy in place. In-house counsel should ensure that they know how to manage and protect their company’s data, including understanding what data is being created, where that data resides, and how to preserve and collect that data when required. This is important not only from an eDiscovery and compliance perspective but also from a data security and privacy perspective. As more jurisdictions across the world enact competing data privacy legislation, it is imperative for organizations to understand what personal data they may be storing and processing, as well as how to collect it and effectively purge it in the event of a request by a data subject.Also, as noted above, the burden to understand an organization’s data storage and preservation strategy does not fall solely on in-house counsel. Outside counsel must also ensure they understand their client’s organizational data to make effective burden, scoping, and strategy decisions during litigation.A Diverse Organization is a Stronger OrganizationFinally, another key theme that has emerged is around recognizing the increasing significance that diversity plays within the legal industry. This year has reinforced the importance of representation and diversity across every industry, as well as provided increased opportunities for education about how diversity within a workforce leads to a stronger, more innovative company. Organizational leaders are increasingly vocalizing the key role diversity plays when seeking services from law firms and legal technology providers. Specifically, many companies have implemented internal diversity initiatives like women leadership programs and employee-led diversity groups and are actively seeking out law firms and service providers that provide similar opportunities to their own employees. The key takeaway here is that organizations and law firms should continue to look for ways to weave diverse representation into the fabric of their businesses.ConclusionWhile this year was plagued by unprecedented challenges and obstacles, the lessons we learned about technology and innovation over the year will help organizations and law firms survive and thrive in the future.To discuss any of these topics more, please feel free to reach out to me at SMoran@lighthouseglobal.com.1 In fact, attorneys already have an ethical duty (imposed by the Rules of Professional Conduct) to understand and utilize existing technology in order to competently represent their clients.ai-and-analytics; ediscovery-review; legal-operationscloud, information-governance, ai-big-data, blog, ai-and-analytics, ediscovery-review, legal-operations,cloud; information-governance; ai-big-data; blogsarah moran

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

Legal operations and process improvements can be tough if you are not speaking the same language. Does the following sound like something you would say? “I'm new to legal operations having come from a business background. Legal has a completely different mindset and even getting people to recognize that we have processes, let alone that we need to improve them, can be difficult. How do I speak to lawyers about process improvement?”If so, you’re in good company. This comment represents a theme I have heard at various legal operations conferences that I have attended. My background as a lawyer turned executive puts me in the position of speaking both lawyer and business professional. Here are some things that, in my experience, have been helpful for legal operations or business professionals entering the world of legal, to know.First, know that the need for a process is not a presumption. Often in the business world, there is general agreement that things should follow a process. That is not the same in legal. There isn’t a presumption for, or against, a process. It isn’t something that is thought about very much and since legal work is different for each matter (i.e. each contract is unique, each litigation is unique), there is a predisposition to thinking things should be done uniquely each time. This predisposition can be overcome but it does warrant an explanation, which is different from the status quo in the business realm.Second, recognize that many lawyers think in terms of risk and not just traditional financial ROI, as many business professionals are taught. For example, a change in a process can be seen as risky because it represents the unknown, so there may be hesitation to change despite a clear financial benefit. The way to overcome this is to consider and quantify the risks of any current process and changes to that process. Much in the way that you would traditionally quantify a financial ROI of anything you’re doing (or not doing), add in the risk factors and mitigations. Third, many lawyers like to see the world in steps from beginning to end – not with a whole bunch of uncertainty in the middle. So, laying things out in a detailed methodical way (e.g., how you will get from where you are now to the final result) will resonate with lawyers. If you do not know all the steps, at least showcasing what you have thought through or when you will have more details will be helpful in overcoming any skepticism.Finally, make sure you’re using a shared language. The meaning of words is very specific in the legal world. How a term has been defined in a contract can be the subject of an entire lawsuit and can make or break a business, so lawyers take definitions very seriously. Making sure everyone is on the same page with respect to the business language you are using can go a long way in avoiding unnecessary confusion. legal-operationslegal-ops, blog, legal-operations-legal-ops; 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

When it comes to storing organizational data in the Cloud, a few phrases come to mind: the train has left the station; the ship has sailed; the horse is out of the barn, etc. No matter how you phrase it, the meaning is the same – the world is moving to the Cloud, with or without you. It is no longer an oncoming revolution. The revolution is here and your organization needs to prepare for dealing with data in the Cloud, if it hasn’t already. With that in mind, let’s talk cloud logistics – namely, security and cost.First up to the Plate – Cloud Security You might have heard the analogy circulating in technology forums recently that storing your data within the Cloud is akin to storing data on someone else’s hard drive. Unfortunately, from a security perspective, that’s not quite an accurate analogy (although life would be much easier if it were true).Don’t get me wrong - a significant benefit of moving to the Cloud is that it allows an organization to transfer much of the day-to-day security management to a technology company with the resources and expertise to handle that risk. Thus, if you are moving to a private cloud (i.e., renting data center space for your equipment), you can ease security concerns by ensuring that the hosting company maintains widely recognized security attestations/certifications and has a demonstrated commitment to data center security in accordance with strict vendor management risk processes. And of course, there’s always the reassurance when moving to a public cloud (Microsoft’s Azure or Amazon’s AWS) that you’re entrusting your data to companies with seemingly infinite security resources and expertise. That all certainly helps me sleep better at night.However, working within the Cloud still poses unique internal security challenges that will only amplify any of your existing security weaknesses if you’re not prepared for them. To put it another way: ISO certifications from cloud service providers cannot protect you from yourself. Risk, governance, and compliance teams will need to identify, plan for and adapt to internal security challenges. To do so, be sure to have a change management and review approval process in place (ideally before moving to the Cloud, but if not, as soon as possible once you’ve migrated). Also, ensure that your company has someone on hand (either through a vendor or within your IT staff) with the expertise needed to manage your internal cloud security who can stay abreast of all updates and changes.Next up – CostTo plan for a cloud migration, all stakeholders (including Legal Operations, Finance, DevOps, Security, and IT) should have a seat at the table and a plan in place for scaling up in the Cloud. Each team should understand the plan and process, as well as the role their team plays in controlling cost and risk for the company.Cloud Security and Costs Best PracticesTo plan for security risk in the Cloud, companies should ensure that:All cloud service providers are fully vetted, security certified, and have the requisite posture in place to fully protect your data.Company internal processes are evaluated for security risks and gaps. Have a change management and review approval process in place and ensure that you have the experts on hand to manage your cloud security practices and stay abreast of all updates and changes.To plan for costs, companies should ensure that:All stakeholders (including Legal Operations, Finance, DevOps, Security, and IT) collaborate and have a plan in place for scaling up within the Cloud when needed.Each team understands the plan and process, as well as the role their team plays in controlling cost and risk for the company.data-privacy; information-governancecloud-security, cloud-migration, blog, data-privacy, information-governancecloud-security; cloud-migration; blogmarcelino hoyla

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

Your team is facing a wall of mounting compliance requirements and internal investigations, as well as a few larger litigations you fear you may not be able to handle given internal resource constraints. Each case involves unwieldy amounts of data to wade through, and that data must be collected from constantly-evolving data sources—from iPhones to Microsoft Teams to Skype chats. You’re working with your IT team to ensure your company’s most sensitive data is protected throughout the course of all those matters.All of this considered, your team is faced with vetting eDiscovery vendors to handle the large litigation matters and ensuring those vendors can effectively protect your company’s data. Simultaneously, you are shouldering the burden of hosting a separate eDiscovery platform for internal investigations with a legal budget that is already stretched thin. Does this sound familiar? Welcome to the life of a modern in-house attorney. Now more than ever, in-house counsel need to identify cost-effective ways to improve the effectiveness and efficiency of their eDiscovery matters and investigations with attention to the security of their company’s data. This is where adopting a cloud-based self-service, spectra eDiscovery platform can help. Below, I’ve outlined how moving to this type of model can ease many of the burdens faced by corporate legal departments.1. The Added Benefit of On-Demand Scalability‍A cloud-based, self-service, spectra platform provides your team the ability to quickly transfer case data into a cutting-edge review platform and access it from any web browser. You’re no longer waiting days for a vendor to take on the task with no insight into when the data will be ready. With a self-service, spectra solution, your team holds the reigns and can make strategic decisions based on what works best for your budget and organization. If your team has the bandwidth to handle smaller internal investigations but needs help handling large litigations, a scalable self-service, spectra model can provide that solution. If you want your team to handle all matters, large and small, but you worry about collecting from unique sources like Microsoft Teams or need help defensibly culling a large amount of data in a particular case, a quality self-service, spectra provider can handle those issues and leave the rest to you. In short, a self-service, spectra solution gives you the ability to control your own fate and leverage the eDiscovery tools and expertise you need, when you need them. 2. Access to the Best eDiscovery Tools – Without the Overhead Costs A robust self-service, spectra eDiscovery solution gives your team access to the industry’s best eDiscovery tools, enabling you to achieve the best outcome on every matter for the most efficient cost. Whether you want to analyze your organization’s entire legal portfolio to see where you can improve review efficiency across matters, or you simply want to leverage the best tools from collection to production, the right solution will deliver. And with a self-service, spectra model, your team will have access to these tools without the burden of infrastructure maintenance or software licensing. A quality self-service, spectra provider will shoulder these costs, as well as the load of continuously evaluating and updating technology. Your team is free to do what it is does best: legal work.3. The Peace of Mind of Reliable Data Security In a self-service, spectra eDiscovery model, your service provider shoulders the data security risk with state-of-the-art infrastructure and dedicated IT and security teams capable of remaining attentive to cybersecurity threats and evolving regulatory standards. This not only allows you to lower your own costs and free up valuable internal IT resources, but also provides something even more valuable than cost savings—the peace of mind that comes with knowing your company’s data is being managed and protected by IT experts.4. Flexible, Predictable Pricing and Lower Overall Costsself-service, spectra pricing models can be designed around your team’s expectations for utilization—meaning you can select a pricing structure that fits your organization’s unique needs. From pay-as-you-go models to a subscription-based approach, self-service, spectra pricing often differs from traditional eDiscovery pricing in that it is clear and predictable. This means you won’t be blindsided at the close of the month with hidden charges or unexpected hourly fees from a law firm or vendor. Add this type of transparent pricing to the fact that you will no longer be shouldering technology costs or paying for vendor services you don’t need, and the result is a significantly lower eDiscovery overhead that can fit within any legal budget. These four benefits can help corporations and in-house counsel teams significantly improve eDiscovery efficiency and reduce costs. For more information on how to move your organization to a self-service, spectra eDiscovery model, be sure to check out our other articles related to the self-service, spectra eDiscovery revolution – including 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