Use access reviews for entitlement hygiene, but pair them with runtime monitoring and contextual policy for AI sessions. If reviews only see historical access, they miss the moment where the AI platform actually exercised privilege, which is where the real governance decision happened.
Why historical access reviews are not enough for AI-enabled sessions
Access reviews answer a governance question about who should have entitlement, but live AI usage answers a runtime question about what privilege was actually exercised. When an AI platform can assume roles, call tools, or act through delegated access, the real control point is the session and its context, not only the static account record. That is why review outcomes can look clean while risky activity still occurs.
For IAM teams, the practical gap is that entitlement certification is retrospective, while AI usage is dynamic. The same approved identity can behave very differently depending on prompt, task, resource, environment, and time window. If your review process does not capture those conditions, it will miss privilege concentration, tool invocation, and cross-context access that only exists during execution.
That is also why Access Reviews and Certification Guide is only the starting point, not the whole control. The same governance logic extends to AI sessions when reviews are meant to remove access rather than merely document it.
What runtime monitoring and contextual policy add to the control model
Runtime monitoring turns AI usage into observable evidence. Instead of asking whether an identity was once approved for access, teams can see which model, agent, user session, or workflow actually touched which resources, for how long, and under which policy conditions. Contextual policy adds the missing decision layer by constraining use based on session state, resource sensitivity, environment, and the specific action requested.
This is especially important when entitlement and execution diverge. An AI session may legitimately start under an approved role, then escalate its practical reach through chained tool calls, inherited tokens, or broad downstream permissions. Runtime controls let teams evaluate the moment of action, which is where governance, auditability, and containment matter most.
IAM and IGA Basics provides the baseline distinction between entitlement administration and authorization governance, while Privileged Access Management Guide covers the runtime controls that become critical once AI sessions can exercise elevated access.
How teams should redesign reviews so they reflect live AI usage
The review process should certify the entitlement, then validate the observed use pattern separately. A good operating model asks three questions: did the identity need the access, did the AI session actually use it, and was that use acceptable for the context in which it happened? If those answers are not captured together, the review result is incomplete.
Teams should also treat AI sessions as first-class audit objects. That means preserving who initiated the session, what policy applied, what tools were available, which resources were reached, and what exceptions were granted. Those details make it possible to detect entitlement drift, overbroad delegation, and usage that is technically authorized but operationally excessive.
Access Reviews and Certification Guide is useful for closing the loop on removals, while Identity Visibility and Intelligence Platforms (IVIP) Guide is the better reference point for correlating review results with observed activity.
Risk and Threat Considerations
When access reviews do not reflect live AI usage, the main risk is false assurance. A team may believe access is governed because the entitlement exists in a clean review record, while the AI platform is actively exercising privilege in ways that were never visible to reviewers. That creates exposure to over-privilege, uncontrolled tool use, and silent expansion of effective access.
Failure mechanism: The review process certifies static access state, but the AI workload uses delegated credentials, session tokens, or inherited permissions at runtime, so the actual access path is never evaluated.
Impact: Unauthorized or excessive AI actions can proceed undetected, audit evidence becomes incomplete, and remediation is delayed until after the session has already affected data, systems, or downstream workflows.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | AU-2 — Audit Events | AI session behavior needs auditable events beyond static entitlement reviews. |
| AC-6 — Least Privilege | Live AI usage can exceed intended access, so privilege scope must be constrained. | |
| IA-5 — Authenticator Management | AI sessions often depend on credentials, tokens, or secrets whose lifecycle affects runtime access. | |
| Recommendation — Log AI session actions, tool calls, and policy decisions as review evidence. Limit AI session permissions to the minimum access required for each task. Track, rotate, and expire credentials that enable AI session access. | ||
| CIS Controls v8 | CIS-6 — Access Control Management | Reviews must be paired with runtime access governance and rights validation. |
| Recommendation — Enforce and verify access rights continuously, not only during certification. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI sessions can misuse delegated authority beyond what reviews show. |
| Recommendation — Constrain and monitor agent privileges during execution. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | AI sessions may operate through non-human identities with excessive effective access. |
| Recommendation — Reduce excessive permissions for AI-connected identities and review their live use. | ||
Practitioner Guidance
What to prioritise: Separate entitlement certification from runtime authorization. If a control only proves that access was granted, treat it as incomplete for AI governance unless it also shows how the session behaved.
What to verify: Confirm that reviews are linked to session logs, tool usage, and policy decisions, not just account-level approval history. If those signals cannot be joined, the review result should not be used as evidence of actual AI control.
Decision rule: If the AI platform can act on behalf of a user or service, review the delegated capability and the observed session together; if it cannot be observed, assume the governance picture is incomplete.
Practitioner takeaway: For AI-enabled access, the meaningful control boundary is the live session, not the paper trail of entitlement alone.
Related resources from NHI Mgmt Group
- How should security teams run access reviews for non-human identities?
- How should security teams govern API keys used for generative AI access?
- Should IAM teams prioritise lifecycle controls or access reviews for AI agents?
- How should security teams govern non-human identities that have persistent access?