Why do NHI and machine identities make IAM measurement harder?

Because they often sit outside the clean lifecycle processes built for human users. Service accounts and API keys are dispersed across systems, frequently over-privileged, and harder to review manually, so posture programmes need continuous discovery and entitlement mapping to avoid blind spots.

Why This Matters for Security Teams

IAM measurement gets harder for NHI and machine identities because the control plane was designed around people, not workloads. Human identities have onboarding, role change, and offboarding events that can be sampled and reviewed. Service accounts, API keys, certificates, and tokens do not follow that rhythm. They are created in pipelines, embedded in code, inherited by tooling, and often left active long after the original purpose has changed. That makes posture metrics such as privilege, ownership, age, and rotation much less reliable unless discovery is continuous and mapped across systems.

This is not a niche problem. NHI Management Group’s Ultimate Guide to NHIs notes that NHIs outnumber human identities by 25x to 50x in modern enterprises, which means even small measurement gaps quickly become large risk gaps. NIST’s SP 800-53 Rev. 5 Security and Privacy Controls gives control structure, but it does not solve the discovery problem by itself. In practice, many security teams discover measurement failures only after a credential leak, service outage, or access review exposes assets that were never fully inventoried.

How It Works in Practice

Measurement improves when teams stop treating NHI as a static inventory and instead measure an identity graph. That means linking workload identity, credential usage, privilege grants, owner information, and rotation state into one view. For example, an API key should be measured not only by whether it exists, but also by where it is used, what it can reach, when it was last rotated, whether it is stored in a secrets manager, and whether the owning service is still live. The Top 10 NHI Issues highlights why this matters: most organisations do not have full visibility into service accounts, and many secrets remain valid long after notification or remediation begins.

Useful measurement usually combines several signals:

• Discovery from cloud, CI/CD, containers, SaaS, and source control.
• Entitlement mapping that ties each secret or workload identity to an owner and business purpose.
• Rotation and expiry metrics that distinguish long-lived credentials from ephemeral ones.
• Usage telemetry that shows which identities are actually active versus merely present.
• Policy checks for excess privilege, third-party exposure, and secrets stored outside approved vaults.

Good measurement also separates workload identity from the credential itself. A certificate or token may be the proof mechanism, but the workload identity is the thing being governed. That is why many programmes now align measurement with zero trust and workload identity principles rather than relying only on periodic access reviews. Guidance from CSA and NIST strongly supports this direction, but the operational model is still evolving in heterogeneous environments. These controls tend to break down when identities are spawned dynamically across ephemeral infrastructure because ownership, logging, and revocation are not consistently wired into the same workflow.

Common Variations and Edge Cases

Tighter measurement often increases operational overhead, requiring organisations to balance visibility against the cost of collecting, normalising, and maintaining the data. That tradeoff is especially visible in environments with hybrid cloud, legacy automation, and third-party integrations, where a single machine identity can be duplicated across accounts or reused by multiple tools.

Current guidance suggests treating these cases differently rather than forcing one universal metric. Short-lived workload identities should be measured by issuance, scope, and revocation latency, while legacy service accounts should be measured by age, privilege breadth, and owner clarity. There is no universal standard for this yet, but best practice is evolving toward contextual measurement, supported by policy and telemetry rather than annual review alone. For deeper breach patterns, the 52 NHI Breaches Analysis shows how exposed keys, over-privileged service accounts, and weak rotation frequently overlap in real incidents.

Edge cases also matter. Shared credentials in build systems can distort counts. Orphaned identities can inflate risk scores. Third-party access can make ownership unclear even when usage is visible. Measurement should therefore be interpreted as a governance signal, not a perfect truth source. Where pipelines generate identities automatically, the quality of measurement depends on whether the creation system emits metadata at the moment of issuance. Without that, reporting becomes retrospective and misses the identities most likely to matter.

Related resources from NHI Mgmt Group

• How should security teams make NHI best practices usable across the business?
• What is the difference between human IAM controls and NHI governance?
• What does the 144:1 NHI-to-human ratio mean for IAM governance programmes?
• Why do AI agents make IAM and NHI risk harder to manage?