Tag Archives: business

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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Filed under Best Practices, compliance, Corporations, Cybersecurity, Data Governance, ECA, eDiscovery & Compliance, Enterprise AI, Enterprise eDiscovery, ESI, GDPR, Information Governance, Records Management

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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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September 16, 2025 · 9:12 AM

X1 Expands Its Leadership in Microsoft Teams eDiscovery Collection

X1 Enterprise MS Teams Collection

By John Patzakis and Chas Meier

The rapid growth of Microsoft 365 has fundamentally changed the eDiscovery landscape. Among its most prominent data sources, Microsoft Teams now generates vast volumes of business-critical communications that must be identified, collected, and reviewed in litigation, regulatory, and compliance matters.

Yet most eDiscovery tools still rely on outdated methods: bulk copying massive amounts of sensitive data and transferring it to proprietary processing or review platforms. This approach is slow, costly, and disruptive. Bulk transfers frequently trigger Microsoft’s throttling controls, adding significant delays. More importantly, organizations that have invested heavily in Microsoft 365 do not want their data routinely exported out of its secure, native environment every time an eDiscovery matter or compliance investigation arises.

Recognizing these challenges, X1 has built upon its industry-leading Microsoft 365 collection capabilities to deliver unmatched support for Microsoft Teams—alongside OneDrive, Exchange, and SharePoint.

Key Benefits of X1’s Teams Collection Capabilities
• Precision targeting of Channels at scale – Quickly search all available channels, select, and target specific Teams channels, even in organizations with tens of thousands of them. This feature is not even available in Microsoft Purview!
• Granular control – Target individual custodians and message threads, avoiding unnecessary mass downloads.
• Contextual collections – Automatically include a designated number of preceding and subsequent messages, preserving conversational context.
• Seamless review integration – One-click upload of fully formatted in-context results directly into review platforms—no manual processing required.
• Unified approach – Search and collect across Teams, OneDrive, SharePoint, Exchange, laptops, and file shares from a single interface.
• In-place indexing – Leverage X1’s patented technology to index, search, and process data where it resides, eliminating reliance on expensive third-party processing.
• True automation – A software-based solution that reduces dependency on manual, service-heavy workflows.

No other independent software provider matches the speed, precision, and scalability of X1’s Microsoft Teams eDiscovery collection. Our customers consistently report significant gains in efficiency, cost savings, and defensibility compared to legacy approaches.

As Teams usage continues to surge, legal and compliance professionals need solutions that deliver targeted, defensible collections without the inefficiencies of bulk exports. X1’s enhanced Teams support ensures organizations can meet these demands with speed, accuracy, and minimal disruption.

Seeing is believing—watch our short demo video to experience X1’s Teams capabilities in action.

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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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