At a recent series of legal technology gatherings, not one attendee signaled that their organization's data was in good enough shape to support artificial intelligence. The uncomfortable silence, reported by Wolters Kluwer ELM Solutions after its Amplify Local Connect events in Toronto, New York, and Chicago, underscores a growing recognition that the biggest obstacle to deploying Microsoft 365 Copilot and other AI tools in legal departments isn't the AI itself—it's the decades of scattered, poorly governed information beneath it.

What the Wolters Kluwer Events Revealed

Wolters Kluwer's Amplify Local Connect events brought legal operations professionals together to discuss AI readiness, with a focus on practical execution. But the conversation kept circling back to a single, uncomfortable question: Can you trust your data? When asked if their organization's data was in good shape, nobody raised a hand.

One attendee noted that inconsistent data quality and insufficient remediation funding left their department unable to confidently introduce AI into workflows. That sentiment captures a critical reality: running a pilot is easy; deploying AI safely at operational scale requires foundational data discipline that many legal departments simply haven't built.

From the discussions, a clear formula emerged: Data → Process → AI. Data supplies the facts, processes define how those facts are created and interpreted, and AI accelerates the work. Reverse the order—deploy AI first—and you get faster answers without knowing if those answers reflect complete records, approved definitions, or appropriate access rights.

Why Governance, Not Model Choice, Is Now the Bottleneck

AI can make information easier to query, but it cannot independently repair duplicate matters, inconsistent vendor names, missing fields, or undefined metrics. Legal data is notoriously complex: it spans structured matter-management systems, unstructured emails, contracts, invoices, and communications across multiple platforms. Matter-management systems may look tidy, but users often leave fields blank, pick the nearest category, or reuse old templates. A single law firm might appear under a dozen different names.

Traditional reporting hid these weaknesses behind a handful of skilled analysts who manually compensated for gaps. Natural-language interfaces rip away that safety net. Now, anyone can ask a question and get a confident-sounding answer, even if the underlying data is a mess. AI democratizes access, but it also scales ambiguity.

Permissions, too, become critical. Most enterprise AI tools respect a user's existing access rights, but many organizations have granted far more access than employees genuinely need. A SharePoint site with inherited permissions from a long-ago project can suddenly expose sensitive documents to an assistant that actively finds and presents them. The security gap isn't that AI bypasses controls—it's that AI makes the consequences of lax controls dramatically more visible.

For most legal departments, the data estate lives inside Microsoft 365. SharePoint holds contracts, Teams hosts privileged discussions, OneDrive stores working drafts, and Outlook carries confidential communications. When Microsoft 365 Copilot arrives on the scene, it will draw from all these sources—unless governance intervenes.

This means endpoint administration and information governance are inseparable from AI strategy. Microsoft Entra ID (formerly Azure AD) manages identities; conditional access policies, multifactor authentication, and role-based access controls determine who can reach what. But group sprawl is rampant. Nested memberships, inactive guest accounts, and legacy security groups create access paths that IT may not even know exist. Principle of least privilege is a familiar mantra, but AI raises the urgency: a dormant permission becomes a live disclosure pathway when an assistant actively discovers content.

Microsoft Purview provides a governance toolkit: sensitivity labels, retention policies, data loss prevention (DLP), and activity monitoring. It can map data, classify sensitive information, and enforce rules. But technology alone cannot decide which records are privileged or what a legal metric means. Legal departments must define the rules; IT can then implement them through Purview. This division of responsibility is essential. IT shouldn't guess at legal meaning, and lawyers shouldn't be handed raw administrative consoles.

Windows endpoints are part of the boundary, too. Employees can copy governed data into browsers, local apps, or unsanctioned AI tools via clipboards, downloads, or screenshots. Endpoint DLP can block or warn, but policies must balance security with usability. A blanket ban may push users toward shadow IT; the stronger approach combines approved tools, clear policy, visible warnings, and realistic training.

Practical Steps to Governance Before AI

Organizations don't need perfect data before every AI experiment, but they must match the use case to the quality and governance of the relevant information. A low-risk pilot using a controlled document set requires less preparation than an enterprise agent with access to privileged communications. Start with targeted use cases, not a universal cleanup.

For each AI use case, identify:

  • The decisions it will support or execute.
  • The systems and documents it will access.
  • Who may view the results.
  • The required completeness and accuracy.
  • Applicable confidentiality and retention rules.
  • Consequences of an incorrect answer.
  • The human review and escalation process.

This yields a bounded data-quality objective. Instead of launching an indefinite cleansing project, fix what the use case genuinely needs.

Next, establish minimum viable governance. Name data owners who have authority to approve definitions, set quality expectations, and accept risk. An owner doesn't have to manage every record, but must resolve disputes and approve changes. Connect business terminology to validation rules: if "high-risk matter" has a definition, make sure users can't label a matter that way without meeting the criteria. Embed controls in systems—mandatory fields, conditional field logic, automatic sensitivity labeling, and approval steps for high-impact actions.

Use Microsoft Purview to:

  • Discover where sensitive data resides across SharePoint, OneDrive, Teams, and Exchange.
  • Apply sensitivity labels automatically based on content patterns.
  • Enforce DLP policies to block or audit sharing of sensitive material.
  • Set retention schedules that align with legal and regulatory requirements.
  • Monitor access and activity, including Copilot interactions.

Testing is non-negotiable. Build a representative evaluation set with straightforward queries, ambiguous requests, missing records, conflicting documents, and restricted content. Test with multiple user identities to verify that permissions are enforced as intended. An AI that says "I can't determine the answer from approved sources" is often safer than one that produces a precise but unsupported number.

Finally, monitor for drift. Data quality decays over time. New matter categories appear, law firms merge, employees develop workarounds. Set up continuous quality controls that trigger a review when completion rates fall, duplicate records rise, or AI correction rates spike.

What's Coming: Agents, Regulation, and Accountability

The shift from AI assistants to autonomous agents raises the stakes. An agent that can plan steps, modify records, and send messages needs bounded authority. Legal teams should separate retrieval privileges from execution privileges and implement a ladder of authority:

  1. AI retrieves and summarizes information.
  2. AI recommends an action with rationale.
  3. AI prepares an action for human approval.
  4. AI executes low-risk actions within defined thresholds.
  5. AI performs higher-risk actions only with additional authorization and monitoring.

Agents need defined scopes, approved data sources, clear success criteria, logging, escalation rules, and a stop condition. A vague instruction like "manage litigation efficiently" is ungovernable; better to task an agent with reviewing invoices for specified billing guideline exceptions and routing flagged items to a human reviewer.

Regulatory expectations are coalescing. The EU AI Act's next major application milestone arrives on August 2, 2026, and frameworks like NIST's AI Risk Management Framework and the ISO/IEC 42001 standard are shaping enterprise programs. Even organizations not directly in scope will feel pressure from customers, vendors, and insurers. Legal departments should document governance measures now: data inventories, ownership records, risk assessments, and incident procedures.

Outlook

Governance has long been seen as the department that says no, adds paperwork, and delays progress. AI flips that narrative. Organizations with trusted information can move faster because they spend less time arguing about whose numbers are right. A mature data catalog, clear ownership, and standardized access reduce the discovery work for each new AI use case. Instead of rebuilding trust from scratch for every pilot, teams can innovate within known boundaries.

For Microsoft 365 shops, the path forward runs through Purview, Entra ID, and a hard look at years of accumulated permissions. The defining question is no longer whether Copilot can produce an impressive answer—it clearly can. The real test is whether your organization can explain what information that answer used, what it excluded, who was allowed to see it, and who remains accountable if it's wrong. Departments that invest in those foundations won't just reduce risk; they'll create the trust required to move from isolated demos to dependable enterprise AI.