Because the review model assumes access changes are visible through human behaviour such as job changes, approvals, or offboarding. AI agents do not naturally create those signals, so stale permissions can persist until a separate control detects them.
Why This Matters for Security Teams
access review are built around a human lifecycle: onboarding, role change, manager approval, and offboarding. AI agents break that assumption because they can be created, cloned, repurposed, and retired without any HR-style event. That means a quarterly recertification can say an entitlement is still approved even while the agent’s task, tool set, or data scope has changed materially. NHI Management Group’s research on the State of Secrets in AppSec shows how security teams often overestimate the maturity of their controls, which matters here because stale permissions are usually discovered only after misuse, not during review.
For AI agents, the problem is not only who should have access, but what the agent is trying to do right now, what tools it can chain together, and whether the current task still matches the original approval. Static review evidence becomes weak when the identity is workload-driven instead of person-driven. In practice, many security teams encounter overprivileged agents only after a tool abuse event, not through intentional recertification.
How It Works in Practice
Effective recertification for agents shifts from periodic approval to continuous validation of workload identity, task scope, and runtime policy. The practical model is closer to zero standing privilege than to traditional IAM: an agent should authenticate as a workload, receive narrowly scoped access only for a specific task, and lose that access automatically when the task ends. Current guidance suggests using workload identity primitives such as SPIFFE or OIDC-backed service tokens, then evaluating permissions at request time with policy-as-code instead of relying on a static role matrix. That aligns with the direction described in the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10.
In practice, the access review evidence should answer different questions than it does for humans:
- What task or goal justified the grant?
- What data, tools, and APIs were in scope?
- What was the token TTL, and was it revoked on completion?
- Did the agent inherit access from another workflow or chain of tools?
- Is there an approver for the policy, not just the identity?
This is why JIT credentials and short-lived secrets matter more for agents than for staff accounts. An agent may be safe at 9 a.m. and dangerous at 9:05 a.m. if the tool chain changes, the prompt is altered, or the workflow begins to self-direct. NHIMG’s coverage of LLMjacking: How Attackers Hijack AI Using Compromised NHIs and CoPhish OAuth Token Theft via Copilot Studio shows why stolen or overbroad agent credentials are operationally dangerous when they can be used immediately. These controls tend to break down in multi-agent pipelines with shared tool access because one agent’s approved action can become another agent’s unintended privilege path.
Common Variations and Edge Cases
Tighter recertification often increases operational overhead, requiring organisations to balance assurance against workflow speed. That tradeoff is real because not every agent needs the same review cadence or approval depth. Best practice is evolving, and there is no universal standard for this yet. For low-risk internal automations, a lightweight task registry plus short TTLs may be enough. For agents touching production systems, customer data, or admin APIs, current guidance favors stronger runtime checks, explicit policy owners, and documented revocation paths.
One common edge case is a long-running agent that spans multiple tasks. A quarterly reviewer may approve the identity once, but the safer pattern is to recertify the policy envelope for each distinct task boundary. Another edge case is delegated access, where an agent acts on behalf of a user but also has independent tool permissions. In those environments, access review can miss the difference between delegated human authority and autonomous machine authority unless both are tracked separately. The OWASP NHI Top 10 and CSA MAESTRO agentic AI threat modeling framework both reinforce the need to model these boundaries explicitly. Access review is least effective when the same agent can be recreated from a prompt, reused across environments, or granted temporary escalation that never shows up as a human lifecycle event.
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, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A03 | Agentic systems need runtime controls, not static human-style recertification. |
| CSA MAESTRO | CTRL-02 | MAESTRO models agent autonomy and tool-use risk that reviews often miss. |
| NIST AI RMF | GOVERN | AI RMF governance applies accountability and oversight to autonomous access decisions. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Short-lived, revocable machine identities reduce stale access in recertification cycles. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access management is directly weakened by persistent agent entitlements. |
Continuously validate agent entitlements and remove permissions that exceed current need.
