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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December 16, 2025 · 9:13 AM

AI Without Data Movement: X1’s Webinar Reveals the Future of Secure Enterprise AI

By John Patzakis

X1’s recent webinar announcing the availability of true “AI in-place” for the enterprise was both highly attended and strongly validated by the audience response. The session did more than introduce a new feature; it articulated a fundamentally different architectural approach to enterprise AI—one designed explicitly for security, compliance, and scalability in complex, distributed environments. Our central message was simple: enterprise AI adoption has been constrained not by lack of interest, but by architectural and security requirements that existing platforms have failed to address.

That reality was most powerfully captured in a quote shared on the opening slide from a Fortune 100 Chief Information Security Officer, which set the tone for the entire discussion:

“Normally AI for infosec and compliance use cases is a non-starter for security reasons, but your workflow and architecture is completely different. This allows us – all behind our firewall — to develop our own models that are trained on our own data and customized to our specific security and compliance use cases and deployed in-place across our enterprise.”

This endorsement crystallized the webinar’s core insight: AI becomes viable for the most sensitive enterprise use cases only when it is deployed where the data already lives, rather than forcing data into external or centralized systems.

The technical foundation that makes this possible is X1’s micro-indexing architecture. Unlike traditional platforms built on centralized, resource-intensive indexing technologies, X1 deploys lightweight, distributed micro-indexes directly at the data source. This allows enterprises to index, search, and now apply AI analysis without mass data movement. As emphasized during the webinar, centralized indexing is not just expensive and slow—it is fundamentally misaligned with how modern enterprise data is distributed across file systems, endpoints, cloud platforms, and collaboration tools.

The session then highlighted how this architectural distinction resolves a long-standing problem in discovery, compliance, and security workflows. Legacy platforms require organizations to collect and centralize data before they can analyze it, introducing delays, high costs, and significant risk exposure. X1 reverses that workflow. By enabling visibility and AI-driven classification before collection, organizations can make informed, targeted decisions—collecting only what is necessary, remediating issues in-place, and dramatically reducing both risk and operational overhead.

The discussion also demystified large language models (LLMs), explaining that while model training is compute-intensive, models themselves are increasingly commoditized and portable. Critically, LLMs require extracted text and metadata— processed from native files—to function. This aligns perfectly with X1’s existing capability, as text and metadata extraction are already integral to our micro-indexing process. AI models can therefore be deployed alongside these indexes, operating in parallel across thousands of data sources with massive scalability.

The conversation then connected this architecture to concrete, high-value use cases. In eDiscovery, AI in-place enables faster early case assessment and proportionality by analyzing data where it resides. In incident response and breach investigations, security teams can immediately scope exposure across distributed systems without waiting months for data exports. For compliance and governance, AI models can continuously identify sensitive data, enforce retention policies, and surface risk conditions that were previously impractical to monitor at scale.

In addition to a live product demo showcasing this new capability, we concluded the webinar with several clarifying points and announcements. First, we emphasized that X1 does not access, monetize, or host customer data. Also, AI in-place is not an experimental add-on but an enhancement to a proven, production-grade platform. And notably, there is no additional licensing cost for the AI capability itself—customers simply deploy models within their own environment. With proof-of-concept testing beginning shortly and production deployments targeted for April 2026, the webinar made clear that AI in-place is not a future vision, but an imminent reality for the enterprise.

You can access a recording of the webinar here, and to learn more about X1 Enterprise, please visit us at X1.com.

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December 3, 2025 · 9:50 AM

Why Most SaaS Architectures Fall Short for Enterprise-Grade AI

By John Patzakis and Chas Meier

SaaS Architectures Fall Short for Enterprise-Grade AI

As organizations accelerate adoption of AI to support legal, compliance, security, and business operations, one principle is becoming clear: the underlying deployment architecture matters as much as the model itself. Many enterprise AI initiatives fail not because the technology is immature, but because the environment in which it operates was never designed for high-volume, sensitive, or tightly regulated use cases.

Traditional multi-tenant SaaS architectures—where numerous customers share the same provider-controlled environment—excel at delivering standardized, lower-risk business applications. But applying that same model to AI workloads involving privileged, regulated, or company sensitive data introduces material limitations in governance, security, performance, and operational feasibility.

Below are the core architectural constraints that legal, IT, and security leaders consistently raise as they evaluate AI strategies.

  1. Data Governance, Privacy, and Regulatory Control
    Most commercial SaaS AI platforms require customer data—or derivative artifacts such as embeddings, logs, or temporary working sets—to be processed within the provider’s environment. Even with strong encryption and contractual controls, this shift of data outside the enterprise’s controlled boundary introduces challenges that many legal and security teams cannot accept.

    Key concerns include:
    • Loss of direct data sovereignty. Once data is inside a vendor’s multi-tenant environment, the organization no longer controls how it is stored, moved, or isolated.
    • Jurisdiction and residency risks. Multi-tenant SaaS services often replicate or route data across regions for load or resilience purposes, complicating GDPR, HIPAA, ITAR, or sector-specific compliance requirements.
    • Governance of secondary artifacts. AI systems often generate embeddings, caches, metadata, and diagnostic logs. Ensuring these artifacts adhere to the same retention, destruction, and legal hold rules become significantly more complex in a shared environment.

    For legal departments, eDiscovery teams, and CISOs, these factors create an expanded compliance burden that is often disproportionate to the value of outsourcing AI workloads.
  2. Assurance of Isolation and Auditability
    Large enterprises increasingly demand verifiable guarantees—not merely assurances—that:
    • Their data is isolated from other tenants
    • Their information is not used for model training unless explicitly authorized
    • Every transaction is auditable and traceable
    • No shared services introduce inadvertent cross-tenant visibility

    While reputable AI providers enforce strong separation controls, multi-tenant architecture inherently increases the assurance burden. The organization must rely on the vendor’s internal controls, certifications, and change management practices—none of which it can independently verify.

    For regulated entities, this can be an unacceptable dependency, particularly where privileged legal data, sensitive communications, or proprietary research is involved.
  3. Performance and Scalability Under AI Workloads
    AI inference and large-scale analysis require sustainable compute performance. Multi-tenant environments, by design, pool capacity across customers. Even when quotas or isolation tiers exist, resource contention and dynamic scaling can introduce variability.

    For enterprise workloads—such as legal investigations, regulatory responses, internal audits, or global compliance monitoring—performance variability translates directly into operational delays and risk.

    Organizations routinely raise:
    • Deterministic performance requirements for time-sensitive matters
    • Workload isolation needs when running tens of thousands of queries or document classifications
    • The high cost of dedicated capacity tiers in third-party SaaS models

    These are structural limitations, not configuration issues.
  4. Data Movement, Transfer Overhead, and Operational Disruption
    Before any SaaS-based AI workflow begins, enterprises must stage or transfer large volumes of data—including emails, documents, chat messages, or historical repositories—into the vendor’s cloud environment.

    This poses several obstacles:
    • Time and bandwidth constraints when transferring terabytes or petabytes
    • Chain-of-custody and legal hold considerations during data movement
    • Jurisdictional restrictions when data cannot transit or be stored outside specific regions
    • Ongoing synchronization challenges as new data is generated

    For legal, compliance, and security teams, these issues often make multi-tenant SaaS unsuitable for high-value unstructured data.
  5. Limited Customization and Restricted Model Control
    Most multi-tenant AI SaaS offerings operate within a shared, standardized stack. This limits an enterprise’s ability to:
    • Tailor models to domain-specific content or workflows
    • Implement custom inference pipelines
    • Integrate internal security, monitoring, or policy engines
    • Maintain visibility into how models process and route sensitive information

    For departments handling privileged, confidential, or regulated data, this lack of deep configurability hampers both innovation and risk mitigation.

The Industry Shift Toward AI-in-Place Architectures
To address these concerns, organizations are increasingly adopting AI-in-Place models—deploying AI capabilities directly onto systems, repositories, and environments they already control.

AI-in-place allows enterprises to:
• Keep all source data behind the firewall or within their private cloud tenancy
• Maintain full sovereignty over models, embeddings, logs, and derived artifacts
• Enforce internal security, retention, and access policies without exception
• Optimize performance around their own infrastructure and workflows
• Reduce compliance complexity by avoiding data egress entirely

This architectural shift reflects a maturing understanding: the value of AI is maximized only when it can operate where sensitive data already resides.

X1 Enterprise: A Modern Foundation for AI-in-Place
X1 Enterprise—with its patented distributed micro-indexing architecture—has emerged as a leading platform for organizations adopting AI-in-Place strategies.

X1 enables:
• In-place analysis without data movement
Deploy LLMs, embeddings, and AI pipelines directly to endpoints, repositories, and cloud data sources—without exporting or copying sensitive content.
• Enterprise-wide visibility across unstructured data
Email, documents, chat, archives, and cloud sources can be searched, tagged, classified, and analyzed at scale from a single federated index.
• High-assurance governance
All data remains within the enterprise’s security boundary or isolated single-tenant cloud, supporting legal holds, audits, discovery, and regulatory requirements.
• Scalable performance tailored to the enterprise’s environment
Micro-indexing distributes compute to where data lives, eliminating bottlenecks inherent in centralized SaaS architectures.

For legal, IT, and security leaders seeking to implement AI responsibly, X1 provides a practical and compliant path forward.

See AI-in-Place in Action
We invite you to join our upcoming webinar on Wednesday, December 10, where our team will present:
• A detailed look at X1’s new AI-in-Place capabilities
• Architectural considerations for legal, IT, and CISO stakeholders
• A live demonstration of enterprise-scale AI applied directly to live data sources

Register here to secure your spot.

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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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September 30, 2025 · 9:45 AM

The Business Case for In-House eDiscovery: Lessons from Two Prominent Corporate eDiscovery Counsel

By John Patzakis

Building a Business and Legal Case for In-House eDiscovery

In a recent webinar hosted by Ad Idem, a non-profit legal education provider for in-house counsel, attorneys Kelly Twigger and Eric Stansell offered a compelling roadmap for corporate legal departments looking to bring eDiscovery and information governance (InfoGov) in-house. Their insights cut through the complexity of traditional discovery models and emphasized the strategic, operational, and legal advantages of internalizing these processes. For legal professionals navigating mounting data volumes and rising litigation costs, their discussion provided both practical guidance and a call to rethink legacy workflows.

Eric Stansell, Senior Counsel for Discovery at Tyson Foods, opened with a candid reflection on how his role was created to address the company’s need for a more efficient eDiscovery program. He emphasized that building a business case for in-house capabilities starts with understanding the “why”—whether it’s cost savings, risk reduction, or process defensibility. Stansell emphasized that standardizing internal processes not only improves consistency but also enhances defensibility and reduces exposure by limiting data sprawl across external vendors.

Kelly Twigger — who is one of if not the top eDiscovery lawyer in the field in my opinion — built on Stansell’s narrative by stressing the importance of conducting a thorough assessment before launching any in-house initiative. She encouraged legal teams to break down business cases into manageable chunks, identifying quick wins such as revising email retention policies. Twigger noted that internal culture shifts and stakeholder alignment are just as critical as technology adoption. Her approach favors incremental change backed by measurable ROI, rather than sweeping transformations that risk overwhelming legal and IT teams.

Both speakers underscored the importance of engaging multiple stakeholders. Stansell shared Tyson’s experience with cross-functional collaboration, highlighting how legal, IT, audit, and compliance teams must be involved from the outset. As one example of such collaboration, Stansell noted that eDiscovery enterprise search and collection software procured by the legal team can also address key IT security priorities such as PII data audits and internal investigations.

Twigger also delivered a deep dive into the proportionality principles now codified under the Federal Rules of Civil Procedure, urging legal teams to build factual arguments early in the discovery process. She explained that proportionality isn’t just about cost—it’s about narrowing scope through targeted custodians, refined date ranges, and iterative search terms. Stansell added that understanding custodians’ roles and historical relevance can help avoid unnecessary data collection, further supporting proportionality claims in court.

One of the most pressing issues Twigger addressed was the evolving case law around hyperlinked files. She traced the trajectory from Nichols v. Noom, Inc.—where hyperlinks were deemed not attachments—to more recent rulings that treat them as discoverable content depending on technological capabilities. Twigger cited In re Uber and Young v. Salesforce to illustrate how courts are increasingly expecting parties to preserve and produce hyperlinked documents, especially when shared via chat platforms or collaborative tools.

Twigger warned that failing to understand your organization’s tech stack could lead to costly missteps. She recommended that in-house counsel proactively assess their systems—especially Microsoft 365 environments—to determine what’s feasible when it comes to hyperlink preservation and production. She also highlighted X1 Discovery’s capabilities, noting that X1’s software can automate the collection of contemporaneous versions of hyperlinked documents in M365, support targeted Teams chat collection as well as many other data sources, making X1 a valuable solution for defensible in-house eDiscovery.

In closing, both Twigger and Stansell made it clear that bringing eDiscovery and InfoGov in-house isn’t just a cost-cutting measure—it’s a strategic imperative. With the right mix of technology, process, and cross-functional collaboration, legal departments can gain control, reduce risk, and improve outcomes. Their insights serve as a blueprint for legal teams ready to evolve beyond reactive discovery and toward a proactive, integrated approach.

The recording of the webinar can be accessed here.

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