Machine identities change the pace of governance because they can be created, used, and abandoned faster than periodic access reviews can respond. IAM teams need live entitlement signals, not only certifications, because service accounts and AI-connected access can outlive the business event that created them. That makes continuous control more useful than retrospective approval.
Why This Matters for Security Teams
Machine identities break the assumptions behind periodic review because they are not static users with stable job roles. A service account, API key, certificate, or workload token can be created for a narrow task, used by automation, then left behind after the business event is over. That makes quarterly or annual certification too slow to catch drift, overprovisioning, or abandoned access before it is exploited.
This is why review processes must shift from "who approved this access?" to "is this identity still needed, and is it still behaving as expected?" The NIST Cybersecurity Framework 2.0 supports this shift by emphasizing continuous risk management rather than one-time checks. NHIMG research also shows how persistent NHI exposure becomes operational debt, especially when secrets live beyond their intended lifecycle, as seen in the Ultimate Guide to NHIs.
In practice, many security teams discover machine identity sprawl only after an incident review reveals that access had been retained long after the workload changed.
How It Works in Practice
IAM teams need to reframe review as an evidence-driven control, not just a certification exercise. For machine identities, the core questions are lifecycle, ownership, entitlement, usage, and revocation. Static review lists are still useful, but they must be fed by live signals from vaults, cloud IAM, CI/CD systems, runtime telemetry, and workload platforms.
Practical review workflows usually include:
- Confirming the business owner and technical owner for each service account or token.
- Checking whether the identity is tied to an active workload, pipeline, or agent.
- Comparing granted permissions to observed usage over a recent window.
- Flagging credentials with excessive scope, no expiry, or no rotation path.
- Revoking identities that no longer map to a current service, job, or integration.
That operating model aligns with NHIMG guidance on the lifecycle processes for managing NHIs, which treats offboarding and rotation as first-class controls rather than cleanup tasks. It also fits the direction of NIST CSF 2.0, where identity governance should support continuous monitoring, not only periodic attestation.
Teams that manage high-change environments often pair review with short-lived credentials, policy-as-code, and automated alerts when a machine identity has not been used for a defined period. That reduces dependence on memory and manual sign-off. These controls tend to break down when identities are embedded directly in legacy applications with no ownership metadata because reviewers cannot reliably distinguish active workload access from stale configuration.
Common Variations and Edge Cases
Tighter machine-identity review often increases operational overhead, requiring organisations to balance strong revocation discipline against application stability and release velocity. Current guidance suggests the review model should vary by identity type, because not every machine identity carries the same risk or lifecycle.
Long-lived integration accounts may still need scheduled certification, but ephemeral pipeline tokens, workload certificates, and AI-agent credentials are better governed through runtime controls and expiry-based checks. There is no universal standard for this yet, so many organisations blend annual recertification with continuous telemetry and exception-based review. That is especially important when identities span cloud, SaaS, and on-premise systems, where ownership and revocation paths are often inconsistent.
The highest-risk edge case is a machine identity that has standing privilege, no clear owner, and no expiry. NHIMG research documents how excessive privilege and weak offboarding remain common failure modes in real environments, which is why teams should treat stale machine identities as active exposure until proven otherwise. Where integrations are deeply embedded, teams may need compensating controls such as scoped vault access, time-bound approval, and automated deprovisioning to avoid disruption while shrinking the review backlog.
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 address the attack and risk surface, while NIST CSF 2.0 and 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 | Addresses stale or mismanaged non-human credentials that reviews must detect. |
| NIST CSF 2.0 | PR.AC-4 | Identity access governance depends on reviewing and limiting active entitlements. |
| NIST AI RMF | AI RMF is relevant when machine identities support autonomous or AI-driven workloads. |
Track machine identity age, ownership, and rotation status, then revoke anything with no valid business need.
Related resources from NHI Mgmt Group
- How do teams judge whether an IAM platform is fit for both human and non-human identities?
- How should security teams compare IAM platforms for both human and non-human identities?
- How should security teams govern non-human identities at scale?
- How should security teams govern non-human identities for compliance?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 11, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
