Review cycles miss the identities that often move the most data and inherit the most privilege. As a result, excessive access can persist in service accounts, pipelines, and AI-connected workflows even when human user access looks clean. The control fails because the review scope is too narrow for the actual access graph.
Why This Matters for Security Teams
Access reviews that ignore machine and AI identities create a false sense of control. Human users may be cleanly recertified while service accounts, pipelines, and agents continue to hold broad permissions, long-lived tokens, and hidden trust paths. That gap matters because NHIs often move faster than people and can touch production data, code, secrets, and infrastructure without a human being in the loop. The issue is not just inventory. It is whether the review actually covers the identities that execute work.
Current guidance from the OWASP Non-Human Identity Top 10 and NHIMG’s Ultimate Guide to NHIs points to the same operational reality: if the identity graph is broader than the review scope, the review cannot be trusted as a control. This is especially true in environments where service-to-service trust, CI/CD automation, and AI-connected workflows accumulate privileges over time. In practice, many security teams discover this only after an audit exception, a lateral movement event, or a leaked token has already exposed the gap.
How It Works in Practice
When access reviews are run correctly, they should include every identity that can authenticate and act: humans, workloads, secrets-backed automation, and autonomous agents. For NHI governance, that means recertifying not just RBAC memberships, but also API keys, certificates, service principals, delegated scopes, and any JIT credentials issued for a task. If a workflow can call tools, access storage, or trigger deployments, it belongs in scope.
Practically, that starts with an inventory that links each NHI to an owner, purpose, environment, and expiry model. Reviews should ask different questions for machine identities than for employees: does the workload still exist, does the secret still need to live, can the privilege be reduced, and can the token be shortened or removed entirely? This is where NHI Lifecycle Management Guide is useful, because lifecycle controls make reviews measurable instead of symbolic. For AI and agentic systems, review scope must also include intent-based authorisation decisions at runtime, not just pre-approved role membership.
That shift matters because static IAM assumptions break when an agent can chain tools, follow a goal, and request new access in context. The control model should move toward workload identity, short-lived secrets, and policy evaluation at request time. In mature environments, teams pair this with PAM for privileged actions, ZSP for standing access elimination, and telemetry that records which identity used which credential and why. NHIMG research on the 52 NHI Breaches Analysis shows why that visibility matters: machine identities are frequently involved in the earliest privilege paths attackers abuse. These controls tend to break down when reviews are periodic but the workload estate is continuously changing because the identity graph drifts faster than the certification cycle.
Common Variations and Edge Cases
Tighter review scope often increases operational overhead, requiring organisations to balance control depth against pipeline velocity and platform complexity. That tradeoff is real, especially in multi-cloud estates, federated SaaS, and agentic ai deployments where identities are created dynamically. The goal is not to force every NHI through a human-style annual certification. Current guidance suggests using risk-based frequency, with high-privilege and internet-facing identities reviewed more often than low-risk internal automation.
There is no universal standard for this yet, but best practice is evolving toward context-aware reviews that distinguish between dormant identities, active production workloads, and autonomous agents with execution authority. For example, a CI/CD bot with deployment rights may only need a short approval chain if its token is ephemeral and tightly constrained, while an AI agent with tool access may require stronger policy gates, stronger logging, and explicit business ownership. This aligns with the intent of the Ultimate Guide to NHIs — Key Challenges and Risks and the DeepSeek breach, both of which underscore how quickly secrets and access can become governance failures when automation is broad and lightly supervised. The practical edge case is systems that generate identities on the fly: if the review process cannot enumerate them reliably, the control will miss the exact identities most likely to carry privileged access.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Reviews must include machine credentials and rotation scope. |
| CSA MAESTRO | Agentic systems need governance beyond human-style access review. | |
| NIST AI RMF | AI governance needs accountability for autonomous access decisions. |
Recertify every NHI secret and revoke standing access when it no longer supports a current workload.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 3, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
