How should organisations govern identity risk when using AI assistants like Microsoft 365 Copilot with enterprise data?

Organisations should treat AI assistants as new access pathways that inherit the permissions, data exposure, and audit requirements of the accounts and systems they can reach. Strong governance means applying least privilege, tightening data access, monitoring prompts and outputs where appropriate, and validating which identities, apps, and connectors can influence results.

Why This Matters for Security Teams

AI assistants such as Microsoft 365 Copilot do not create a separate trust domain by default. They sit on top of existing tenant permissions, content labels, connector access, and application trust. That means identity risk is not limited to the human user who launches the prompt. It also includes the identities, service principals, and integrations that can shape what the assistant can see, retrieve, or disclose. NHI Management Group’s Ultimate Guide to NHIs notes that only 5.7% of organisations have full visibility into their service accounts, which is a serious problem when AI assistants depend on those same back-end identities.

Security teams often underestimate the way enterprise search, shared drives, mailboxes, and connected apps become prompt-adjacent access paths. The result is that overly broad identity scopes can surface sensitive material into answers, summaries, or drafts even when no traditional data exfiltration event is obvious. The NIST Cybersecurity Framework 2.0 remains useful here because it forces teams to tie identity governance to asset visibility, access control, and monitoring rather than treating copilots as a separate product problem. In practice, many security teams discover identity overexposure only after an assistant has already summarized data that should never have been reachable in the first place.

How It Works in Practice

Governance starts by mapping which identities and connectors can influence the assistant’s output. That includes the end user, delegated permissions, Graph or API scopes, shared mailbox access, Teams and SharePoint permissions, and any third-party connectors or custom apps. The principle is simple: if an identity can read, write, or invoke something in the workspace, an AI assistant may be able to reflect that capability in a response. Current guidance suggests applying least privilege to both humans and non-human identities, then reviewing whether those permissions are actually needed for the assistant’s intended business use.

A practical control pattern looks like this:

• Restrict data sources before rollout, rather than relying on user training after launch.
• Classify high-risk repositories and exclude them from assistant reach where possible.
• Review app registrations, OAuth grants, and service principals that can extend the assistant’s reach.
• Monitor unusual prompt activity, connector usage, and data access patterns where telemetry is available.
• Validate offboarding and rotation processes for secrets that support integrations behind the assistant.

This is where NHI discipline matters. The CoPhish OAuth Token Theft via Copilot Studio research shows how identity abuse can pivot through assistant ecosystems, while NIST SP 800-53 Rev 5 Security and Privacy Controls provides the control language for access enforcement, logging, and configuration management. The right question is not only what the user is allowed to see, but what every connected identity can make visible through the assistant. These controls tend to break down when legacy permissions, sprawling connectors, and unmanaged service accounts all feed the same assistant experience.

Common Variations and Edge Cases

Tighter assistant governance often reduces productivity, so organisations have to balance data exposure against usable automation. That tradeoff becomes sharper in highly collaborative tenants where permissions are already broad and content is spread across many repositories. Best practice is evolving, but current guidance suggests using tiered rollout models so that sensitive business units, regulated data sets, and external collaboration spaces are handled separately rather than exposed to a single global policy.

Edge cases matter. A Copilot-style assistant may appear safe in a pilot tenant and become risky after additional connectors, custom plugins, or cross-tenant sharing are introduced. Similarly, a user’s access may be appropriate on paper while the assistant still exposes over-permissive data because inherited group membership, nested roles, or stale service credentials were never cleaned up. NHIMG’s Top 10 NHI Issues and Ultimate Guide to NHIs — Regulatory and Audit Perspectives are useful reminders that auditability and lifecycle control need to extend to machine identities supporting the assistant, not just the human account in front of it. There is no universal standard for this yet, but organisations should treat connector sprawl, stale privileges, and weak offboarding as immediate red flags rather than theoretical concerns.

Related resources from NHI Mgmt Group

• How should financial institutions govern AI use without weakening identity and data protection controls?
• Why do AI assistants like Copilot create governance risk in IAM programmes?
• How should organisations govern sensitive data moving outside Microsoft 365?
• How can organisations reduce risk when deploying AI assistants with sensitive data access?