Category Archives: eDiscovery & Compliance

USDOJ Expects Companies to Proactively Employ Data Analytics to Detect Fraud

By John Patzakis and Craig Carpenter

In corporate fraud enforcement actions, The US Department of Justice considers the effectiveness of a company’s compliance program as a key factor when deciding whether to bring charges and the severity of any resulting penalties. Recently, prosecutors increased their emphasis on this policy with new evaluation guidelines about what prosecutors expect from companies under investigation.DOJ

The USDOJ manual features a dedicated section on assessing the effectiveness of corporate compliance programs in corporate fraud prosecutions, including FCPA matters. This section is a must read for any corporate compliance professional, as it provides detailed guidance on what the USDOJ looks for in assessing whether a corporation is committed to good-faith self-policing or is merely making hollow pronouncements and going through the motions.

The USDOJ manual advises prosecutors to determine if the corporate compliance program “is adequately designed for maximum effectiveness in preventing and detecting wrongdoing by employees and whether corporate management is enforcing the program or is tacitly encouraging or pressuring employees to engage in misconduct to achieve business objectives,” and that “[p]rosecutors should therefore attempt to determine whether a corporation’s compliance program is merely a ‘paper program’ or whether it was designed, implemented, reviewed, and revised, as appropriate, in an effective manner.”

Recently, Deputy Assistant Attorney General Matthew Miner provided important additional guidance through official public comments establishing that the USDOJ will be assessing whether compliance officers proactively employ data analytics technology in their reviews of companies that are under investigation.

Miner noted that the Justice Department has had success in spotting corporate fraud by relying on data analytics, and said that prosecutors expect compliance officers to do the same: “This use of data analytics has allowed for greater efficiency in identifying investigation targets, which expedites case development, saves resources, makes the overall program of enforcement more targeted and effective.” Miner further noted that he “believes the same data can tell companies where to look for potential misconduct.” Ultimately, the federal government wants “companies to invest in robust and effective compliance programs in advance of misconduct, as well as in a prompt remedial response to any misconduct that is discovered.”

Finally, “if misconduct does occur, our prosecutors are going to inquire about what the company has done to analyze or track its own data resources—both at the time of the misconduct, as well as at the time we are considering a potential resolution,” Miner said. In other words, companies must demonstrate a sincere commitment to identifying and investigating internal fraud with proper resources employing cutting edge technologies, instead of going through the motions with empty “check the box” processes.

With these mandates from government regulators for actual and effective monitoring and enforcement through internal investigations, organizations need effective and operational mechanisms for doing so. In particular, any anti-fraud and internal compliance program must have the ability to search and analyze unstructured electronic data, which is where much of the evidence of fraud and other policy violations can be best detected.

But to utilize data analytics platforms in a proactive instead of a much more limited reactive manner, the process needs to be moved “upstream” where unstructured data resides. This capability is best enabled by a process that extracts text from unstructured, distributed data in place, and systematically sends that data at a massive scale to an analytics platform, with the associated metadata and global unique identifiers for each item.  One of the many challenges with traditional workflows is the massive data transfer associated with ongoing data migration of electronic files and emails, the latter of which must be sent in whole containers such as PST files. This process alone can take weeks, choke network bandwidth and is highly disruptive to operations. However, the load associated with text/metadata only is less than 1 percent of the full native item. So the possibilities here are very compelling. This architecture enables very scalable and proactive solutions to compliance, information security, and information governance use cases. The upload to AI engines would take hours instead of weeks, enabling continual machine learning to improve processes and accuracy over time and enable immediate action to be taken on identified threats or otherwise relevant information.

The only solution that we are aware of that fulfills this vision is X1 Enterprise Distributed GRC. X1’s unique distributed architecture upends the traditional collection process by indexing at the distributed endpoints, enabling a direct pipeline of extracted text to the analytics platform. This innovative technology and workflow results in far faster and more precise collections and a more informed strategy in any matter.

Deployed at each end point or centrally in virtualized environments, X1 Enterprise allows practitioners to query many thousands of devices simultaneously, utilize analytics before collecting and process while collecting directly into myriad different review and analytics applications like RelativityOne and Brainspace. X1 Enterprise empowers corporate eDiscovery, compliance, investigative, cybersecurity and privacy staff with the ability to find, analyze, collect and/or delete virtually any piece of unstructured user data wherever it resides instantly and iteratively, all in a legally defensible fashion.

X1 displayed these powerful capabilities with Compliance DS in a recent webinar with a brief but substantive demo of our X1 Distributed GRC solution, emphasizing our innovative support of analytics engines through our game-changing ability to extract text in place with a direct feed into AI solutions.

Here is a link to the recording with a direct link to the 5 minute demo portion.

In addition to saving time and money, these capabilities are important to demonstrate a sincere organizational commitment to compliance versus maintaining a mere “paper program” – which the USDOJ has just said can provide critical mitigation in the event of an investigation or prosecution.

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Filed under Best Practices, compliance, Corporations, Data Audit, eDiscovery & Compliance, Information Governance

In-Place Data Analytics For Unstructured Data is No Longer Science Fiction

By John Patzakis

AI-driven analytics supercharges compliance investigations, data security, privacy audits and eDiscovery document review.  AI machine learning employs mathematical models to assess enormous datasets and “learn” from feedback and exposure to gain deep insights into key information. This enables the identification of discrete and hidden patterns in millions of emails and other electronic files to categorize and cluster documents by concepts, content, or topic. This process goes beyond keyword searching to identify anomalies, internal threats, or other indicators of relevant behavior. The enormous volume and scope of corporate data being generated has created numerous opportunities for investigators seeking deep information insights in support of internal compliance, civil litigation and regulatory matters.

The most effective use of AI in investigations couple continuous active learning technology with concept clustering to discover the most relevant data in documents, emails, text and other sources.  As AI continues to learn and improve over time, the benefits of an effectively implemented approach will also increase. In-house and outside counsel and compliance teams are now relying on AI technology in response to government investigations, but also increasingly to identify risks before they escalate to that stage.

Stock Photo - Digital Image used in blog

However, logistical and cost barriers have traditionally stymied organizations from taking advantage of AI in a systematic and proactive basis, especially regarding unstructured data, which, according to industry studies, constitutes 80 percent or more of all data (and data risk) in the enterprise. As analytics engines ingest the text from documents and emails, the extracted text must be “mined” from their native originals. And the natives must first be collected and migrated to a centralized processing appliance. This arduous process is expensive and time consuming, particularly in the case of unstructured data, which must be collected from the “wild” and then migrated to a central location, creating a stand-alone “data lake.”

Due to these limitations, otherwise effective AI capabilities are utilized typically only on very large matters on a reactive basis that limits its benefits to the investigation at hand and the information within the captive data lake.  Thus, ongoing active learning is not generally applied across multiple matters or utilized proactively. And because that captive information consists of migrated copies of the originals, there is a very limited ability to act on data insights as the original data remains in its actual location in the enterprise.

So the ideal architecture for the enterprise would be to move the data analytics “upstream” where all the unstructured data resides, which would not only save up to millions per year in investigation, data audit and eDiscovery costs, but would enable proactive utilization for compliance auditing, security and policy breaches and internal fraud detection.  However, analytics engines require considerable computing resources, with the leading AI solutions typically necessitating tens of thousands of dollars’ worth of high end hardware for a single server instance. So these computing workloads simply cannot be forward deployed to laptops and multiple file servers, where the bulk of unstructured data and associated enterprise risk exists.

But an alternative architecture solves this problem. A process that extracts text from unstructured, distributed data in place, and systematically sends that data at a massive scale to the analytics platform, with the associated metadata and global unique identifiers for each item.  As mentioned, one of the many challenges with traditional workflows is the massive data transfer associated with ongoing data migration of electronic files and emails, the latter of which must be sent in whole containers such as PST files. This process alone can take weeks, choke network bandwidth and is highly disruptive to operations. However, the load associated with text/metadata only is less than 1 percent of the full native item. So the possibilities here are very compelling. This architecture enables very scalable and proactive compliance, information security, and information governance use cases. The upload to AI engines would take hours instead of weeks, enabling continual machine learning to improve processes and accuracy over time and enable immediate action to taken on identified threats or otherwise relevant information.

The only solution that we are aware of that fulfills this vision is X1 Distributed GRC. X1’s unique distributed architecture upends the traditional collection process by indexing at the distributed endpoints, enabling direct pipeline of extracted text to the analytics platform. This innovative technology and workflow results in far faster and more precise collections and a more informed strategy in any matter.

Deployed at each end point or centrally in virtualized environments, X1 Enterprise allows practitioners to query many thousands of devices simultaneously, utilize analytics before collecting and process while collecting directly into myriad different review and analytics applications like RelativityOne and Brainspace. X1 Enterprise empowers corporate eDiscovery, compliance, investigative, cybersecurity and privacy staff with the ability to find, analyze, collect and/or delete virtually any piece of unstructured user data wherever it resides instantly and iteratively, all in a legally defensible fashion.

X1 displayed these powerful capabilities with ComplianceDS in a recent webinar with a brief but substantive demo of our X1 Distributed GRC solution, emphasizing our innovative support of analytics engines through our game-changing ability to extract text in place with direct feed into AI solutions.

Here is a link to the recording with a direct link to the 5 minute demo portion.

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Filed under Best Practices, collection, compliance, Corporations, eDiscovery & Compliance, Enterprise eDiscovery, Enterprise Search, GDPR, Uncategorized

Government Regulators Reject “Paper” Corporate Compliance Programs Lacking Actual Enforcement

By John Patzakis

Recently, US Government regulators fined Stanley Black & Decker $1.8m after its subsidiary illegally exported finished power tools and spare parts to Iran, in violation of sanctions. The Government found that the tool maker failed to “implement procedures to monitor or audit [its subsidiary] operations to ensure that its Iran-related sales did not recur.”

Notably, the employees of the subsidiary concealed their activities by creating bogus bills of lading that misidentified delivery locations and told customers to avoid writing “Iran” on business documents. This conduct underscores the importance of having a diligent internal monitoring and investigation capability that goes beyond mere review of standard transactional records in structured databases such as CRM systems. This type of conduct is best detected on employee’s laptops and other sources of unstructured data through effective internal investigations processes.Law Journal2

The Treasury Department stated the Stanley Black & Decker case “highlights the importance of U.S. companies to conduct sanctions-related due diligence both prior and subsequent to mergers and acquisitions, and to take appropriate steps to audit, monitor and verify newly acquired subsidiaries and affiliates for….compliance.”

Further to this point, the US Department of Justice Manual features a dedicated section on assessing the effectiveness of corporate compliance programs in corporate fraud prosecutions, including FCPA matters. This section is a must read for any corporate compliance professional, as it provides detailed guidance on what the USDOJ looks for in assessing whether a corporation is committed to good-faith self-policing or is merely making hollow pronouncements and going through the motions.

The USDOJ cites United States v. Potter, 463 F.3d 9 (1st Cir. 2006), which provides that a corporation cannot “avoid liability by adopting abstract rules” that forbid its agents from engaging in illegal acts, because “[e]ven a specific directive to an agent or employee or honest efforts to police such rules do not automatically free the company for the wrongful acts of agents.” Id. at 25-26. See also United States v. Hilton Hotels Corp., 467 F.2d 1000, 1007 (9th Cir. 1972) (noting that a corporation “could not gain exculpation by issuing general instructions without undertaking to enforce those instructions by means commensurate with the obvious risks”).

The USDOJ manual advises prosecutors to determine if the corporate compliance program “is adequately designed for maximum effectiveness in preventing and detecting wrongdoing by employees and whether corporate management is enforcing the program or is tacitly encouraging or pressuring employees to engage in misconduct to achieve business objectives,” and that “[p]rosecutors should therefore attempt to determine whether a corporation’s compliance program is merely a ‘paper program’ or whether it was designed, implemented, reviewed, and revised, as appropriate, in an effective manner.”

With these mandates from government regulators for actual and effective monitoring and enforcement through internal investigations, organizations need effective and operational mechanisms for doing so. In particular, any anti-fraud and internal compliance program must have the ability to search and analyze unstructured electronic data, which is where much of the evidence of fraud and other policy violations can be best detected.

To help meet the “actual enforcement” requirements of government regulators, X1 Distributed Discovery (X1DD) enables enterprises to quickly and easily search across up to thousands of distributed endpoints and data servers from a central location.  Legal and compliance teams can easily perform unified complex searches across both unstructured content and metadata, obtaining statistical insight into the data in minutes, and full results with completed collection in hours, instead of days or weeks. Built on our award-winning and patented X1 Search technology, X1DD is the first product to offer true and massively scalable distributed data discovery across an organization. X1DD replaces expensive, cumbersome and highly disruptive approaches to meet enterprise investigation, compliance, and eDiscovery requirements.

Once the legal team is satisfied with a specific search string, after sufficient iteration, the data can then be collected by X1DD by simply hitting the ‘collect’ button. The responsive data is “containerized” at each end point and automatically transmitted to either a central location, or uploaded directly to Relativity, using Relativity’s import API where all data is seamlessly ready for review. Importantly, all results are tied back to a specific custodian, with full chain of custody and preservation of all file metadata. Here is a recording of a live public demo with Relativity, showing the very fast direct upload from X1DD straight into RelativityOne.

This effort described above — from iterative, distributed search through collection and transmittal straight into Relativity from hundreds of endpoints — can be accomplished in a single day. Using manual consulting services, the same project would require several weeks and hundreds of thousands of dollars in collection costs alone, not to mention significant disruption to business operations. Substantial costs associated with over-collection of data would mount as well, and could even dwarf collection costs through unnecessary attorney review time.

In addition to saving time and money, these capabilities are important demonstrate a sincere organizational commitment to compliance versus maintaining a mere “paper program.”

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Filed under Best Practices, Case Law, Case Study, compliance, Corporations, eDiscovery & Compliance, Enterprise eDiscovery, Information Governance

In addition to TAR, CAR Can Dramatically Reduce Attorney Review Costs

eDiscovery efforts are often costly, time consuming and burdensome. The volume of Electronically Stored Information is growing exponentially and will only continue to do so. Even with the advent of technology assisted review (TAR), the costs associated with collecting, processing, reviewing, and producing documents in litigation are the source of considerable pain for litigants. The only way to reduce that pain to its minimum is to use all tools available in all appropriate circumstances within the bounds of reasonableness and proportionality to control the volumes of data that enter the discovery pipeline.

Litigators and commentators often pine for the advent of a systemized, uniform and defensible process for custodian self-collection. Conceptually, such an ideal process would be where custodians are automatically presented with a set of their documents and emails that are identified as potentially relevant to a given matter through a set of keywords and other search parameters that are uniformly applied across all custodians. This set of ESI would be presented to the custodian in a controlled interface with no ability to delete documents or emails, and only the ability to review and apply tags and annotations. The custodian would have to comply with the order and all documents responsive to the initial unified search would be collected as a default control mechanism.

With X1 Data Audit and Compliance (XDAC), the option for a defensible custodian assisted review (CAR) is now a reality. At a high level, with XDAC, organizations can perform targeted search and collection of the ESI of thousands of endpoints over the internal network without disrupting operations. The search results are returned in minutes, not weeks, and thus can be highly granular and iterative, based upon multiple keywords, date ranges, file types, or other parameters. This approach typically reduces the eDiscovery collection and processing costs by at least one order of magnitude (90%), thereby bringing much needed feasibility to enterprise-wide eDiscovery collection that can save organizations millions while improving compliance. XDAC includes X1 Insight and Collection for pure eDiscovery use cases.

As a key optional feature, XDAC provides custodian assisted review, where custodians are presented with a listing of their potentially relevant ESI in a controlled, systemized and uniform identification process for their review and tagging. Instead of essentially asking the custodians to “please rummage through your entire email account and all your documents to look for what you might think is relevant to this matter,” the custodians are presented with a narrow and organized subset of potentially relevant ESI for their review.

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While the custodians are able to assist with the review, they cannot impact or control what ESI is identified and preserved; this is controlled and managed centrally by the eDiscovery practitioner. This way, custodians can apply their own insight to the information and even flag personal private data, all while effectuating very cost-effective and systematic ESI collection.

Powerful Analytics Engine

TAR features powerful algorithms that cluster documents and otherwise work their magic. CAR also relies on a powerful analytics engine — the human brain. Custodians know a lot about their own documents and emails. This is particularly true in technical or other complex matter where the custodians are engineers or other professionals who simply better understand the dynamics and the nuances of their information. With the X1 process, the custodians provide a key data point, where their input is used to inform the secondary review.

The process is very defensible as the exercise is logged and documented, with all metadata kept intact and a concise chain of custody established. Best of all, the custodian-applied tags and annotations are preserved and retained through the review process with X1 integration with Relativity. I could describe this very important feature a lot further, but candidly the best way to get a full picture is to see it for yourself. I recommend that you view this recorded 9 minute demonstration of X1’s custodian self-review feature here.

We believe X1’s functionality provides the optimal means for enterprise eDiscovery preservation, collection and early data assessment, especially with the key additional (and optional) feature of custodian assisted review. But please see for yourself and let us know what you think!

 

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Filed under Best Practices, compliance, Desktop Search, eDiscovery & Compliance, Enterprise eDiscovery

eDiscovery Veteran Craig Carpenter Joins X1 as CEO

Today we have some very exciting news: Craig Carpenter has joined X1 as our new CEO. Craig is a seasoned and experienced executive in the eDiscovery and information governance arena, holding several senior executive positions throughout his impressive career, including CEO, EVP of Sales, CMO, COO, and General Counsel.  Craig was an early executive team member of one of the pioneers of eDiscovery software, Recommind, and a key part of that story from startup to creation of an industry-leading brand.  Craig Carpenter 250 sq

Most recently Craig was CEO of Fronteo, which he joined after running sales at Kroll Ontrack. He now brings his proven, customer-centric approach to X1. What I especially like about Craig is his track record as a thought leader and innovator in the legal technology arena. Here is what another thought leader, UK lawyer and renowned eDiscovery expert Chris Dale said last year on his blog, The eDisclosure Project, about Craig:

“Craig Carpenter was personally responsible for much of Recommind’s success in promoting its expertise at predictive coding. This was a tough sell in those days when it was new, and Craig Carpenter tackled the promotional task with vigour. He was also the first person I heard (at a conference in Hong Kong) predict that analytics had a big future role to play in information governance, something which reached fruition when, a long time later, OpenText added Recommind to its stable with precisely that intention in mind.”

I couldn’t agree more. In fact, Craig will be weighing in on very soon with a blog post of his own on why he came to X1, which will include further insight into what Chris Dale says above, so stay tuned. Craig is a central part of X1’s recent growth and expansion. Our X1 eDiscovery and information governance platform has caught fire, fueled in part by our game-changing alliance with Relativity announced earlier this summer. And LegalTech News weighed in today about Craig, noting that “there are few people more equipped to predict the future of e-discovery.” In his interview with Legaltech News, Carpenter said X1’s position aligns well of with his vision of the e-discovery marketplace, where the goal is to “move the strategy formation phase up to the beginning, as opposed to later in the process.”

Besides being a well-known thought leader in eDiscovery, Craig also has forensic technology experience as he served as Chief Marketing Officer and COO of AccessData. Craig began his career as a practicing attorney. He earned both his Juris Doctor and MBA from Santa Clara University (my law school alma mater) and completed his undergraduate studies at UCLA, where he played quarterback on the Bruins’ Pac-10 championship football team. A team, which I might add, could really use his help this season, but I digress.

And speaking of interesting blog posts, look for even more quality content on this site as Craig will be a featured co-blogger going forward. For now, I am very excited to welcome Craig onboard to take the X1 helm!

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Filed under compliance, eDiscovery, eDiscovery & Compliance, Enterprise eDiscovery, Uncategorized