Why do machine identities create problems for traditional IAM reviews?

Why This Matters for Security Teams

Traditional IAM reviews were built for people with logins, sessions, and predictable review cadences. Machine identities do not behave that way. Service accounts, API keys, workload tokens, and certificates can persist across pipelines, clouds, and applications long after the original request is forgotten. NIST’s NIST Cybersecurity Framework 2.0 helps teams think in terms of ongoing risk management, but it does not remove the core operational challenge: review boards often lack the context needed to judge whether a non-human credential is still necessary.

The problem is not only volume. It is ambiguity. Ownership may be unclear, purpose may be undocumented, and expiry may not be tied to any real lifecycle event. That means access can look legitimate on paper while remaining active in production. NHIMG research shows the scale of the gap: only 19.6% of security professionals express strong confidence in their organisation's ability to securely manage non-human workload identities, and 88.5% say their non-human IAM practices lag behind or merely match human IAM. In practice, many security teams discover risk only after a leaked secret or privilege abuse has already occurred, rather than through a deliberate review process.

Cases like JetBrains GitHub plugin token exposure show how a credential can remain useful to an attacker long after a human owner assumes it is harmless.

How It Works in Practice

Machine identities create problems for IAM reviews because review logic often assumes a stable owner, a clear user-to-role mapping, and a credential that can be judged by job title or department. Those assumptions break down for workloads. A build job may assume a role for minutes, an integration may need access only during a deployment window, and an AI agent may request tools dynamically based on runtime context. For that reason, current guidance suggests shifting from periodic entitlement review toward continuous validation of identity, purpose, and expiry.

Practical review should start with inventory. Teams need to know what the identity is, what workload uses it, what systems it touches, where the secret lives, and when it should expire. That is where non-human identity governance differs from traditional IAM: the control is less about recertifying a person and more about proving that a credential still matches an active workload need. NHIMG’s Ultimate Guide to NHIs notes that 97% of NHIs carry excessive privileges and that only 5.7% of organisations have full visibility into service accounts, which explains why review meetings often miss the real blast radius.

• Link each machine identity to a named workload, repository, pipeline, or service owner.
• Record purpose, system scope, expiry, and rotation path for every secret or token.
• Prefer short-lived credentials over static keys where the platform allows it.
• Validate that privileges still match actual runtime behaviour, not just historical approval.
• Revoke dormant identities automatically when the workload is retired or replaced.

This is also where secrets hygiene matters. Credentials exposed in code, config, or CI/CD tooling can bypass review entirely, which is why Azure Key Vault privilege escalation exposure is a useful reminder that indirect access paths are often the real failure point. These controls tend to break down in large hybrid and multi-cloud environments because ownership, expiry, and policy enforcement are not consistently propagated across platforms.

Common Variations and Edge Cases

Tighter review of machine identities often increases operational overhead, requiring organisations to balance faster change delivery against stronger accountability. That tradeoff is especially visible in CI/CD, ephemeral workloads, and service meshes, where credentials may need to be created and revoked automatically rather than manually approved.

There is no universal standard for this yet, but best practice is evolving toward context-aware review. Instead of asking only whether access was approved, teams should ask whether the credential is still needed, whether its TTL is appropriate, and whether the identity can be tied to a live workload. This is especially important for shared service accounts, multi-tenant platforms, and third-party integrations, where a single identity may support several technical functions and one stale approval can mask multiple risks.

Traditional IAM review also struggles when human ownership changes faster than the machine identity lifecycle. A team can reorg, a pipeline can be cloned, or an API integration can outlive its original business use case. In those cases, the review artifact may still look clean while the access path remains active. The practical response is to combine NHI governance with runtime enforcement and continuous secret rotation, not rely on quarterly recertification alone.

Where policy needs to account for autonomous or agentic workloads, the standard answer becomes even less reliable because access can change during execution, and static role reviews cannot predict every tool chain or escalation path.

Related resources from NHI Mgmt Group

• Why does standing privilege create risk for both workforce and machine identities?
• Why do non-human identities complicate traditional IAM programmes?
• Why do non-human identities create more audit risk than human accounts?
• How should security teams run access reviews for non-human identities?