What should teams do when AI starts helping with access reviews?

Why This Matters for Security Teams

When AI begins assisting with access reviews, the risk is not that it “makes decisions” too well. The risk is that it inherits weak identity data, unclear ownership, and inconsistent entitlement models, then accelerates them at machine speed. Access review is only trustworthy when the underlying identity and privilege records are trustworthy. Without that, AI becomes a force multiplier for bad governance rather than a control improvement.

This is why security teams need to separate review assistance from review authority. Current guidance from the OWASP Non-Human Identity Top 10 and NHI lifecycle practices from NHI Lifecycle Management Guide both point to the same operational reality: identity hygiene comes first, automation second. AI can surface anomalies, cluster similar access patterns, and reduce analyst fatigue, but it cannot infer a valid entitlement model where none exists.

NHIMG research on secrets and identity exposure shows how quickly weak governance compounds; in the State of Secrets in AppSec, GitGuardian & CyberArk reported that the average estimated time to remediate a leaked secret is 27 days, despite strong confidence in secrets management. In practice, many security teams encounter AI-assisted review failures only after stale entitlements or missing owners have already been approved at scale, rather than through intentional control testing.

How It Works in Practice

AI should be introduced into access reviews as a decision-support layer, not as a replacement for accountable reviewers. The practical pattern is to let AI pre-process large entitlement sets, highlight outliers, and draft review recommendations, while keeping policy exceptions, privileged access approvals, and ownership disputes under human approval. That division of labour should be explicit in policy, not implied by workflow.

Teams usually get better results when they anchor review automation to authoritative identity sources, then define what the model may use. That includes authoritative HR and IAM records, entitlement catalogs, application ownership metadata, and control-plane logs. If the dataset is incomplete, AI can still help triage, but it should be limited to advisory output. This is consistent with the operational direction in the Ultimate Guide to NHIs, which treats identity provenance and lifecycle discipline as prerequisites for reliable governance.

• Verify owner, role, and business justification before allowing AI to recommend removal or retention.
• Use AI to flag anomalies such as duplicate access, privilege creep, and dormant entitlements.
• Keep privileged, system, and break-glass accounts on a stricter human approval path.
• Log every AI-assisted recommendation, the source evidence used, and the human disposition.

For standards alignment, the OWASP Non-Human Identity Top 10 helps frame identity risk, while NIST AI governance guidance reinforces that AI outputs need oversight proportional to impact. These controls tend to break down when entitlement data is spread across multiple directories and shadow applications because the model cannot reconcile conflicting sources of truth.

Common Variations and Edge Cases

Tighter AI-assisted review often increases operating overhead, requiring organisations to balance review speed against approval quality. That tradeoff becomes more visible in environments with multiple tenants, outsourced administration, or heavily automated infrastructure, where access relationships change faster than manual attestations can keep up.

There is no universal standard for how much autonomy to give AI in access recertification yet, so current guidance suggests a conservative rollout. Start with low-risk entitlements, read-only recommendations, and anomaly detection, then expand only after the organisation has validated data quality and exception handling. High-risk paths should remain human-approved, especially when access enables production change, customer data exposure, or privileged secret use.

Edge cases usually involve non-human identities rather than employees. Service accounts, workload identities, and agentic systems often have access that does not map cleanly to a job title or manager approval. In those cases, review criteria should focus on workload purpose, runtime constraints, and credential lifetime rather than traditional role descriptions. The 52 NHI Breaches Analysis is a useful reminder that unmanaged non-human access often persists longer than human reviewers expect.

When AI is used to summarise review evidence, teams should still require explicit decision records for revoke, retain, and escalate outcomes. Otherwise, the automation may create a false sense of closure while leaving ownership gaps unresolved. Best practice is evolving, but one principle is stable: AI can accelerate access reviews only after the organisation has made its identity data trustworthy enough to review.

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?
• When is it crucial to implement least-privilege access for AI agents?