How should security teams use agentic AI to improve access reviews without replacing their IGA platform?

Why This Matters for Security Teams

Agentic AI changes access reviews because it does not behave like a person with stable job functions. It can chain tools, traverse systems, and surface entitlements that look harmless in isolation but become risky when combined at runtime. That makes traditional certification comments and spreadsheet-style attestations too blunt for the speed and variability of AI-driven access. Current guidance suggests reviewers need machine-assisted context, not a replacement for the IGA system of record.

This is especially important where access is tied to NHIs, service tokens, or agent workflows that already blur ownership. NHIMG research on AI Agents: The New Attack Surface report shows how often agent behaviour exceeds intended scope, while the OWASP Agentic AI Top 10 highlights the need to evaluate agent actions, not just identities. In practice, many security teams discover review gaps only after an agent has already inherited excessive access through inherited roles, stale entitlements, or a tool integration nobody reassessed after launch.

How It Works in Practice

The most effective pattern is to let agentic AI act as an analytical layer in front of the IGA workflow, not as the authority that approves access. The IGA platform remains the source of record for entitlement data, reviewer assignments, policy decisions, and audit evidence. The agent then ingests access snapshots, role memberships, last-used signals, peer group baselines, ticket history, and application context to pre-score risk and explain why a certification item deserves attention.

That means reviewers see fewer noise-heavy entries and more targeted prompts such as outlier privilege, dormant access, toxic combinations, shared accounts, or entitlements that do not match the user, workload, or business justification. This is aligned with the NIST AI Risk Management Framework, which treats governance as a lifecycle process, and with CSA MAESTRO agentic AI threat modeling framework, which emphasises agent behaviour in context.

• Use the agent to cluster low-risk access into review bundles and isolate exceptions.
• Have it flag mismatches between declared purpose and effective permissions.
• Require human approval for removals, recertification, and remediation actions.
• Log every recommendation, input signal, and reviewer override for auditability.

Where this becomes stronger than static rules is in the runtime narrative it creates around access: why the entitlement exists, what the agent observed, and whether the pattern is normal for that population. That is especially useful for workloads and NHIs that do not fit clean HR categories, including cases discussed in Moltbook AI agent keys breach and the OWASP Non-Human Identity Top 10. These controls tend to break down when entitlement data is fragmented across multiple directories and the agent cannot reliably reconcile the same identity across systems.

Common Variations and Edge Cases

Tighter review automation often increases governance overhead, requiring organisations to balance faster certification against model drift, false positives, and reviewer trust. Best practice is evolving, and there is no universal standard for how much autonomy an access-review agent should have before it becomes part of the control decision itself.

In mature environments, teams often start with read-only recommendations, then move to queued remediation suggestions, and only later allow limited auto-routing of low-risk items. In higher-risk environments, especially those involving privileged access, production secrets, or customer data, the safer design is to keep the agent advisory and require explicit human sign-off on every exception. That caution is reinforced by NHIMG reporting on Replit AI Tool Database Deletion and the OWASP NHI Top 10, both of which reinforce that autonomous systems can act beyond intended scope.

Security teams should also separate certification of human access from governance of agent-issued access. An IGA platform may know who a user is, but the agentic layer must also reason about what the agent can do, what tools it can invoke, and whether its output is trustworthy enough to drive downstream decisions. Where the identity data model cannot distinguish a person, an application, and an autonomous agent cleanly, access review automation tends to produce confident but misleading recommendations.

Related resources from NHI Mgmt Group

• How should security teams use user list views to speed up access reviews without losing control of critical details?
• How should security teams run access reviews for non-human identities?
• How should security teams govern API keys used for generative AI access?
• How should security teams use AI agents for user access reviews?