Tag Archives: e-discovery

February 12, 2026 · 10:15 AM

Navigating Legal and Compliance Risks When Corporations Expose Sensitive Data to AI

By Kelly Twigger and John Patzakis

Implementing AI within a corporate environment is no longer a matter of “if” but “how.” We recently addressed these challenges in our webinar, “Navigating Legal and Compliance Risks in AI,” where our panel of experts discussed the strategic transition required to build a robust risk mitigation framework. While the efficiency gains of AI—such as automating workflows and surfacing deep insights—are compelling, introducing sensitive enterprise data into these models without a tactical plan can lead to unintended consequences. These risks range from the dilution of trade secrets to complex eDiscovery obligations and substantial regulatory exposure under the GDPR.

To leverage AI safely, counsel should focus on the following grounded strategies for risk management.

Protect Trade Secrets
Under federal law, trade secret status is contingent upon the owner taking “reasonable measures” to maintain secrecy. This is a rigorous standard; if proprietary information—such as source code or high-value technical data—is fed into an unsecured AI model without strict access controls, a company risks losing its legal protections entirely.

  • Review the Judicial Standard: In Snyder v. Beam Technologies, Inc., the 10th Circuit affirmed that failing to use confidentiality protections or allowing information to reside on unsecured devices can defeat trade secret status.
  • Maintain Active Safeguards: Courts emphasize that consistent and active safeguards are required to maintain secrecy. Lax internal controls during AI interactions can be cited as evidence that “reasonable measures” were not maintained.
  • Implement No-Prompt Zones: Establish “No-Prompt Zones” for your organization’s most sensitive intellectual property. By isolating core IP from third-party cloud models, you maintain a defensible record of “reasonable measures” that can withstand scrutiny in litigation.

Manage the eDiscovery Paper Trail
AI interactions—both the prompts submitted by employees and the responses generated by the tools—are considered discoverable Electronically Stored Information (ESI). These records are part of the corporate record and are subject to subpoena and legal holds.

  • Understand the Technical Reality: Microsoft has confirmed that Microsoft 365 Copilot interactions are logged through the Purview unified audit log, making them searchable, preservable, and producible via eDiscovery tools.
  • Assess Scope of Exposure: Because these chats are treated no differently than emails, they may inadvertently expose privileged or damaging material if not managed properly.
  • Map Information Logs: Update your legal hold workflows to specifically include AI conversation logs and audit trails. Mapping where these logs live before litigation arises ensures a more controlled and cost-effective discovery process.

Navigate GDPR and Data Privacy
Processing customer or employee data through AI models requires strict adherence to the GDPR principles of data minimization, purpose limitation, and lawfulness. Feeding sensitive data into AI models without a clearly articulated lawful basis—such as consent or legitimate interest—can result in significant administrative fines.

  • Meet Compliance Requirements: European authorities require organizations to demonstrate compliance by documenting purposes, limiting data inputs, and ensuring appropriate safeguards are in place.
  • Identify Special Categories: The GDPR is particularly restrictive regarding health information or data revealing racial or ethnic origin, requiring specific exemptions for processing.
  • Conduct Privacy Impact Assessments: Perform mandatory Privacy Impact Assessments (PIAs) for any AI tool that touches personal data. Documenting the purpose and necessity of the processing is critical for maintaining regulatory standing during an audit.

Leverage In-Place AI Functionality
A critical strategy for reducing risk is shifting where the AI processing occurs. Rather than routing data through external, third-party cloud-hosted AI services, organizations should consider prioritizing workflows where AI is applied in-place within the corporate network or controlled enterprise environment.

  • Secure the Data Perimeter: By keeping data and AI processing behind the organization’s own security firewall, you materially reduce the risk of trade secret leakage and data exfiltration.
  • Minimize Third-Party Footprint: Applying AI in-place narrows the scope of discoverable third-party records, as the interactions remain within your internal infrastructure rather than residing on a vendor’s servers.
  • Establish Full Governance Control: This model provides counsel with direct control over privacy, retention, and audit obligations—essentially giving you the “kill switch” for data that you simply do not have with external cloud vendors.

Tactical Governance and Ethical Oversight
Counsel must navigate the professional and technical nuances of AI deployment to ensure long-term stability.

  • Ensure Professional Competence: The ethical duty of technological competence requires attorneys to understand the limitations of the tools they use. AI should be treated as a “junior associate”—capable of great speed but requiring diligent human verification of all output.
  • Apply Risk-Based Tiering: Not all AI use cases carry the same weight. We recommend a tiered approach:
    o Tier 1 (Administrative): Low-risk tasks involving non-sensitive data.
    o Tier 2 (Internal/Marketing): Standard communications requiring routine oversight.
    o Tier 3 (High-Value/Restricted): High-stakes processing involving PII, health data, or proprietary IP, requiring senior legal sign-off and strict data handling protocols.
  • Execute Proactive Vendor Vetting: Move from consumer-grade tools to enterprise solutions that offer SOC 2 Type 2 attestations. Ensure contracts explicitly prohibit the vendor from using your data to train their global models.

In light of these risks, corporate counsel should take a proactive, structured approach to AI governance. This includes implementing data classification and usage controls to prevent sensitive trade secrets from being exposed to AI systems without safeguards; establishing clear policies governing AI prompts, outputs, retention, and eDiscovery treatment; and conducting privacy impact assessments to ensure personal data processing complies with GDPR and similar regulations. In addition, counsel should carefully evaluate AI deployment models and consider workflows in which AI models are deployed in-place within the corporate network or controlled enterprise environment, rather than routed through third-party cloud-hosted AI services. Keeping data and AI processing inside the organization’s security perimeter can materially reduce trade secret leakage risk, narrow the scope of discoverable third-party records, and provide greater control over privacy, retention, and audit obligations—while still allowing the enterprise to realize the benefits of advanced AI capabilities.

For a deeper dive into these strategies and more case studies, you can watch the full session here.

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November 13, 2025 · 11:13 AM

X1 Brings “AI In-Place” to the Enterprise—A Major Breakthrough for Secure, Scalable AI Deployment

By John Patzakis

Our latest announcement represents a true inflection point in enterprise AI. With X1 Enterprise’s newly introduced capability for AI in-place, organizations and their service providers will, for the first time, be able to deploy and execute large language models (LLMs) directly where enterprise data lives—without moving or copying that data.

This is more than a product enhancement; it is a fundamental shift in how AI is applied across the enterprise.

The Foundation: Efficient Text Extraction Is Critical for AI
Large language models (LLMs) are the core engines that power today’s AI revolution. These models rely entirely on textual input to perform reasoning, summarization, search, and analysis. That is why text extraction is the critical first step. LLMs can only operate once another process extracts the text from emails, documents and chats. Traditionally, that meant copying or exporting data to external systems hosted by third party vendors, a process fraught with risk, cost, and compliance challenges.

Solving the “Data Movement Problem” for Enterprise AI
So, the key barrier to enterprise AI adoption has been the reluctance to move sensitive corporate data to external AI platforms. Whether for security, governance or cost reasons, most enterprises simply cannot send their data outside their environment.

X1’s innovation solves that problem head-on. Instead of shipping sensitive data out to an AI system, X1 brings the AI to the data. Enterprises can now deploy their own proprietary models or open-source LLMs within the secure perimeter of their existing infrastructure, whether on premises or in the cloud. X1’s index-in-place architecture performs the text extraction and indexing where the data resides. By extending that same principle to AI—forward-deploying LLMs directly to enterprise data sources—X1 now enables AI in-place. The result: organizations can apply the analytical power of LLMs across their data without ever moving it.

Once the LLMs are deployed into the X1 micro-indexes, X1 will then auto-apply AI-informed tags, which a user can query globally from a central console and act upon through targeted data collection or remediation. Imagine petabytes of data on file servers, laptops M365 and other sources all AI-classified and then queried and collected on a highly targeted basis.

This means enterprises can now unlock powerful new use cases no matter the scale—AI-assisted compliance, risk monitoring, GRC audits, eDiscovery, and more—while maintaining full control of their data and eliminating the need for costly, risky data transfers.

Enabling Collaboration Between Enterprises and Their Advisors
William Belt, Managing Director and Consulting Practice Leader at Complete Discovery Source, described the impact succinctly:

“Enabling AI in-place where our corporate client’s data lives is game-changing. We look forward to working with our clients to deploy AI models that are either pre-trained or customized for a specific matter or compliance requirement utilizing the X1 Enterprise platform.”

This capability creates a new bridge between corporations and their professional advisors—consulting firms, law firms, and service providers—who can now collaborate directly with their clients to develop, fine-tune, and deploy customized AI models for specific business or legal needs.

Rather than relying on generic cloud-based AI tools, organizations can now build targeted, matter-specific LLMs that are tuned to their unique data and compliance requirements, all executed securely in-place through the X1 Enterprise Platform.

A New Era for Enterprise AI
With this release, X1 is redefining the architecture of enterprise AI. Its ability to perform distributed micro-indexing and in-place AI analysis across global data sources enables secure, scalable, and cost-effective intelligence—without ever duplicating or relocating sensitive data.

For enterprises and their partners, this represents a new era of possibility: true AI at enterprise scale, in-place.

X1 will host a webinar on Wednesday, December 10, featuring a detailed overview of this new capability and a live demonstration. You can register here.

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Filed under Cloud Data, Corporations, Cybersecurity, eDiscovery, eDiscovery & Compliance, Enterprise AI, Enterprise eDiscovery, Information Governance, m365

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August 5, 2025 · 9:57 AM

Why Most eDiscovery Tools and Online Archiving Offerings Are Terrible for Information Governance

By John Patzakis and Chas Meier

Many organizations assume that information governance initiatives—such as data privacy audits, purging ROT (Redundant, Obsolete, or Trivial) data, merger and acquisition-driven data separation, or data breach impact assessments—can be effectively addressed using eDiscovery tools or online archiving platforms. After all, eDiscovery solutions excel at identifying and searching through large volumes of unstructured data in high-stakes, reactive legal scenarios.

However, there is a critical distinction between eDiscovery and information governance workflows that organizations must understand when selecting the right solution. eDiscovery typically involves copying large volumes of data at multiple stages and continually moving that data upstream, eventually into third-party cloud platforms for processing and hosting. In contrast, duplicating and moving massive data sets is often the last thing you want to do in information governance projects, which are typically large-scale, enterprise-wide initiatives.

In fact, here are five major reasons why most eDiscovery tools and online archiving solutions are terrible for information governance. These tools:

  1. Dramatically Increase Risk
    Consider a scenario where an organization suffers a data breach and must assess 100 terabytes of data to identify compromised PII and determine reporting obligations. Most eDiscovery tools require a full copy of this data to be made and uploaded into a third-party environment—doubling the volume of sensitive material and compounding the risk. Instead of helping, this kind of mass data duplication exacerbates the compliance and privacy risks that governance initiatives aim to reduce. In fact, such inefficient data duplication directly conflicts with GDPR principles, which require data minimalization and proportionality.
  2. Are Exorbitantly Expensive
    Information governance is not a small, tactical effort—it is a broad, enterprise-wide initiative. At X1, we rarely see governance projects involving less than 50 terabytes of data. Using traditional eDiscovery pricing models, even with volume-based discounts, these projects can quickly rack up tens of millions of dollars in costs due to unnecessary processing, storage, and hosting workflows designed for litigation—not governance.
  3. Can’t Meet Time Constraints
    Copying, transferring, uploading, and indexing 100 terabytes of data into a third-party cloud platform can easily take six months or more, even in an ideal scenario. That timeline is incompatible with the urgent nature of most information governance use cases, such as data breach impact assessments or M&A-related audits. Worse yet, by the time the data has been copied and indexed, it will likely already be stale—undermining the integrity of the project from the outset.
  4. Create Remediation Roadblocks
    Suppose you incur the costs and risk to copy and upload a full data set in an external review platform and successfully identify sensitive or outdated data for remediation. Now what? You are merely working with copies of the data. The originals remain distributed across Microsoft 365, file servers, laptops, and other locations. Trying to trace back and manually remediate live data sources is costly, disruptive, and error-prone—defeating the very efficiency goals of the governance project.
  5. Do not Support Microsoft 365 Effectively
    Many so-called “governance” tools are simply rebranded email archiving systems that rely on bulk copying data out of Microsoft 365. Not only is this approach expensive and inefficient, but it also creates serious technical and compliance risks. Microsoft 365 does not support mass data exports at scale without significant friction, and errors are common—as illustrated in FTC v. Match Group, No. 3:19-CV-2281-K, 2025 WL 46024 (N.D. Tex. Jan. 7, 2025). In that case, Microsoft Purview exports into an archival system failed, resulting in court-imposed discovery sanctions. If a solution does not support index-in-place capabilities—allowing analysis directly upon the native data—it is simply not viable for modern information governance needs.

A Different Approach is Required
Information governance requires agility, precision, and a fundamentally different approach than traditional eDiscovery processes. Organizations must be wary of legacy eDiscovery tools and outdated archiving platforms masquerading as governance solutions.

X1 Enterprise was purpose-built to address the challenges and inefficiencies that plague traditional eDiscovery tools and archiving platforms when applied to information governance. At the core of the X1 Enterprise Platform is its patented micro-indexing architecture, which enables organizations to search, analyze, and act on data in place, without needing to first copy, move, or centralize it.

This index-in-place capability means X1 can connect directly to endpoints, file shares, Microsoft 365, and other enterprise data sources to perform fast, scalable, and highly targeted data sweeps and analysis—without duplicating the data or exposing it to unnecessary risk. Whether you are performing a data privacy audit, a breach impact assessment, or an M&A data separation project, you can run real-time searches across tens of terabytes and thousands of custodians—with results returned in minutes, not months, and the data remediation performed in-place.

By eliminating the need for data movement, X1 avoids the five major pitfalls of legacy tools:
• Risk: No mass duplication of data, reducing exposure and aligning with GDPR and other regulatory requirements.
• Cost: No massive ingestion or hosting fees—X1 dramatically lowers total project costs by working directly with live data.
• Time: Deploy and execute governance initiatives in a fraction of the time required by traditional methods.
• Remediation: Act directly on live data—flag it, move it, delete it, or apply tags—in the original source locations.
• Microsoft 365 Compatibility: X1 integrates natively with Microsoft 365 and other systems without requiring cumbersome exports or expensive additional licensing and services, enabling robust, reliable governance at enterprise scale. Simply put, we believe X1 provides the best available support for M365 data sources.

In short, X1 Enterprise offers a faster, safer, and far more cost-effective way to execute complex information governance projects—turning what used to be massive, reactive, months-long efforts into streamlined, proactive, and strategic workflows.

Learn more about how X1 Enterprise can streamline your next information governance project. Schedule a demo today at sales@x1.com or visit www.x1.com/solutions/x1-enterprise-platform.

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June 25, 2025 · 12:00 PM

Modernizing eDiscovery: A Huge Strategic Win for Legal Operations Executives

By John Patzakis

Modern In-Place Data Discovery

For today’s corporate legal departments, controlling runaway costs is no longer optional — it’s a mandate. Nowhere is this more evident than in the spiraling expenses for outsourced eDiscovery and information governance services. While litigation and regulatory demands continue to grow, many organizations still rely heavily on costly outside service providers to identify, collect, process, and produce electronically stored information (ESI). This outdated model drains budgets, strains timelines, and introduces unnecessary risk.

Enter the modern legal operations executive. One of their core responsibilities is to identify inefficiencies and leverage technology to reduce costs and streamline workflows. Modernizing eDiscovery and information governance processes is a very fertile and high-impact opportunity to do exactly that. Doing so can save organizations tens of millions of dollars in hard (actual) costs. Here’s how:

1) Bring eDiscovery In-House and Slash Costs with the Right Technology

Outsourced eDiscovery vendors typically charge steep hourly rates and volume-based markups for even routine tasks like identifying and collecting custodial data. Yet studies — and real-world case studies — consistently show that corporations can reduce eDiscovery costs by up to 90% by adopting targeted collection and in-place search technology.

Solutions like X1 Enterprise enable legal and compliance teams to index and search data in place — without cumbersome, time-consuming manual collection. By deploying this technology internally, the legal operations team can replace costly third-party workflows, including highly inefficient Microsoft 365 processes, with faster, defensible, and far less expensive processes. This means greater control over timelines and budgets, and reduced exposure to data security risks associated with handing over large volumes of sensitive information to multiple vendors.

2) Drive Broader Efficiencies Beyond Litigation

The benefits of a modern eDiscovery platform extend far beyond document production in a lawsuit. The same technology can be leveraged for critical information governance and data compliance functions. For example, when a company needs to respond to internal audits, regulatory data access requests, or data privacy audits and inquiries, in-place search capabilities allow teams to quickly find and manage relevant data without reinventing the wheel each time.

Legal operations executives can champion the use of enterprise eDiscovery tools for these broader use cases, creating synergies between compliance, privacy, IT, and legal teams. This not only reduces redundant spending on separate point solutions but also ensures better control of data and improved risk management across the organization.

3) Partner with Finance to Uncover Hidden Cost Savings

A key role of legal operations is to align legal spend with broader corporate financial goals. When evaluating an in-house eDiscovery solution, legal ops leaders should engage their CFO early. One common pitfall is focusing solely on capital IT budgets while overlooking how much is siphoned away from the legal operating budget to fund expensive outsourced eDiscovery services.

In one real-world example, a company assumed they could not afford an internal solution based on their limited IT budget. However, when they worked with their CFO to analyze total eDiscovery spending, they discovered they were paying tens of millions annually from a separate operating budget to outside providers. Redirecting even a fraction of this spend towards a robust internal platform not only paid for the technology but will yield millions in net savings — year after year.

Final Thoughts

For legal operations executives looking to deliver immediate cost savings, increase efficiency, and elevate the department’s strategic value, modernizing eDiscovery and information governance processes is perhaps their greatest opportunity for an immediate and significant impact. By bringing the process in-house with proven technology like X1 Enterprise, expanding its use to multiple compliance and governance scenarios, and partnering with finance to eliminate wasteful spending, legal operations can transform eDiscovery and information governance from a financial drain into a model of operational excellence.

Interested in learning more about how to achieve this transformation? Schedule a briefing today at sales@x1.com or visit www.x1.com/solutions/x1-enterprise-platform.

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June 17, 2025 · 2:56 PM

X1 Enterprise Is the Gold Standard for Data Separation in M&A Matters

By John Patzakis and Charles Meier

X1 is the Gold Standard in Data Separation

Corporate mergers and acquisitions are complex enough on their own — but when a deal involves the divestiture of an entire business unit or a carve-out of specific departments, the stakes for separating data correctly and efficiently become even higher. Legal and IT teams must identify and surgically separate emails, documents, and other unstructured electronic information to ensure that the right data goes to the acquiring party — and that what must be retained remains secure and compliant with privacy and legal requirements.

This data separation exercise is notorious for being time-consuming, extremely expensive, and highly disruptive. This is because traditional methods require heavy lifting by IT teams and service providers, endless back-and-forth with custodians, and mass data collections that literally double the risk. Worse yet, Microsoft Purview, with its known throttling and low throughput challenges for M 365 data, is not up to the task for data separation matters that invariably involve at least dozens of terabytes. These inefficiencies all lead to severe regulatory risks, runaway costs, and critical delays.

There is, however, a far better way — X1 Enterprise. Several major corporations have recently employed X1 Enterprise in high-stakes data separation matters. Once completed, the comments from our customers are the same: There was no other way they could have done it without spending millions of dollars on time-consuming and disruptive services.

Data Separation Is Not Just Another eDiscovery Project

Unlike standard eDiscovery, a divestiture-driven data separation project must carve out large volumes of live, operational data while the business continues to run. Legacy tools and processes require copying and moving the entire subject data set to a separate repository for indexing and searching — adding huge costs, time delays, and operational risk.

X1 Enterprise’s game-changing advantage lies in its distributed micro-indexing architecture and true index-in-place capability. This unique approach allows organizations to instantly search, categorize, and separate or otherwise remediate massive volumes of data where it resides — without duplicating and exporting entire data sets to third-party servers for processing.

In practical terms, this means:

Lightning-Fast Search: X1 Enterprise creates lightweight, local micro-indexes on endpoints and servers across the organization. Search results come back in seconds, no matter where the data lives — on laptops, file shares, or cloud repositories such as M365.

Minimal Disruption: Because the data stays in place, there is no need to duplicate or move sensitive content, minimizing the risk of data leakage, avoiding the bottlenecks that come with data copying and migration for centralized processing, and enabling the actual remediation to be infinitely more effective by working on the live data set. How do you execute data separation when you are working off a stale copy of the data for the categorization effort? The short answer: Up to millions of dollars in manual services to go back to the “original data” and manually separate the data for each employee and their respective data sources.

Scalability and Control: Whether the divestiture involves hundreds or thousands of custodians across geographies, X1 Enterprise scales seamlessly while giving legal and IT teams centralized control and real-time oversight.

Defensible Process: Legal teams can generate audit trails, reports, and logs to demonstrate a precise and defensible chain of custody, which is critical for regulatory and contractual compliance.

The Bottom Line: Much Faster, with Dramatically less Cost and Risk.

When time is money — and delays can put entire deals at risk — organizations cannot afford cumbersome, legacy eDiscovery workflows for carve-out data separation projects. X1 Enterprise’s innovative architecture empowers legal, compliance, and IT teams to execute precise data separations faster, with dramatically lower cost and business impact.

For any organization facing a merger, acquisition, or divestiture, X1 Enterprise is not just an upgrade — it is the modern standard for high-stakes data separation and governance.

Learn more about how X1 Enterprise can streamline your next M&A project. Schedule a demo today at sales@x1.com or visit  www.x1.com/solutions/x1-enterprise-platform.

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