How should security teams use AI agents for user access reviews?

Why Security Teams Need a Different Model for AI-Driven Access Reviews

AI agents can accelerate user access reviews by collecting evidence, correlating entitlements, and drafting recommendations, but the control itself is not just a workflow problem. The harder issue is that agents are autonomous, goal-driven software entities with execution authority, which means their access patterns can shift as tasks change. Static RBAC alone is a poor fit when the reviewer is not human and the evidence trail must survive audit scrutiny.

This is why current guidance suggests treating the agent as a constrained NHI, not as a substitute approver. In practice, the agent should operate under least-privilege read access, explicit workload identity, and short-lived credentials rather than standing secrets. That maps cleanly to OWASP NHI Top 10 and the OWASP Agentic AI Top 10, both of which emphasize that agentic systems create new paths for overreach, tool abuse, and hidden privilege escalation.

Organisations that ignore this usually discover the problem after the agent has already been trusted to aggregate access data across systems it should never have been able to reach in the first place.

How It Works in Practice

The practical pattern is to let the agent assist with evidence, not own the decision. Security teams can give the agent read-only access to identity sources, ticket history, and application logs, then require human reviewers to approve or reject the recommendation. That keeps the final authority with accountable reviewers while still using automation to reduce manual effort.

For agentic access review workflows, three design choices matter most. First, issue Ultimate Guide to NHIs-style workload identity to the agent so the system can verify what it is, not just what credentials it presents. Second, prefer just-in-time, ephemeral secrets over persistent API keys, because autonomous systems can chain actions faster than human operators can intervene. Third, evaluate authorisation at runtime with policy-as-code rather than relying on a pre-set role that becomes stale as the agent’s objective changes.

That approach aligns with the NIST AI Risk Management Framework and the CSA MAESTRO agentic AI threat modeling framework, both of which reinforce governance, traceability, and context-aware control placement. A useful operational pattern is to log the agent’s intent, the data sources queried, the diffs it produced, and the reviewer who signed off, then route exceptions into a manual escalation queue. The SailPoint report AI Agents: The New Attack Surface found that only 52% of companies can track and audit the data their AI agents access, which is exactly the kind of visibility gap that makes access reviews unreliable when agents are involved.

These controls tend to break down in sprawling SaaS environments where the agent can inherit broad connector privileges and silently traverse systems that were never designed for granular, request-time policy evaluation.

Common Variations and Edge Cases

Tighter control often increases operational overhead, so organisations need to balance review speed against the risk of turning an agent into an opaque decision engine. There is no universal standard for how much autonomy is acceptable here, and current guidance suggests using the agent for evidence assembly, not final adjudication, until the workflow has been proven stable and auditable.

One common edge case is exception handling. If a reviewer asks the agent to investigate an unusual entitlement, the agent may need temporary access to a broader set of logs or systems. That should be granted through JIT provisioning with explicit expiry, not through permanent expansion of the agent’s standing access. Another edge case is cross-domain review, where HR, IAM, and application owners each expect different evidence. In those cases, the workflow should preserve source-of-truth boundaries and avoid letting the agent blend datasets in ways that would violate purpose limitation or internal policy.

For deeper threat context, review AI LLM hijack breach alongside the NIST AI Risk Management Framework. Where teams are experimenting with fully automated approvals, the risk is not only incorrect access decisions but also hidden privilege expansion driven by the agent’s task objective. That is why best practice is evolving toward human accountable approval, runtime policy checks, and per-task secrets rather than standing permissions.

Related resources from NHI Mgmt Group

• How should security teams govern AI agents that use OAuth access?
• How should security teams limit the risk from AI agents that have access to production systems?
• How should security teams govern AI agents that can access enterprise systems?
• How should organisations use AI agents in access reviews without losing governance control?