How should security teams measure identity security maturity across human and machine identities?

Security teams should measure maturity across governance, tooling, operating model, and talent, then test whether those controls cover both human and machine identities. A strong programme can describe ownership, review cadence, policy enforcement, and exception handling for each identity class. If those elements are inconsistent, the organisation has partial coverage rather than mature identity security.

Why This Matters for Security Teams

Identity security maturity is only meaningful if it covers the full control plane: people, service accounts, API keys, workloads, and the systems that issue and review access. Human identity programmes often have mature joiner-mover-leaver processes, while machine identities remain scattered across code, pipelines, and cloud services. That gap creates false confidence because governance looks complete on paper but fails where secrets, rotation, and offboarding matter most.

Current benchmarks show how wide that gap is. NHI Management Group’s Ultimate Guide to NHIs reports that only 5.7% of organisations have full visibility into their service accounts, which is why maturity measurements must test evidence, not intent. The broader control model should also align to the NIST Cybersecurity Framework 2.0, which emphasises governance, identification, protection, detection, response, and recovery across assets and identities.

In practice, many security teams discover that their identity programme is mature for employees but only partially controlled for machines after secrets leak, a service account is overprivileged, or a cloud workload is abused for lateral movement.

How It Works in Practice

A useful maturity model compares human and machine identities across the same four dimensions: governance, tooling, operating model, and talent. The difference is in the evidence. For humans, security teams can verify policy acknowledgements, access reviews, and approval workflows. For machine identities, they should verify where secrets live, how workload identity is issued, whether credentials are short-lived, and whether offboarding happens automatically when a workload, pipeline, or application is retired.

The most reliable approach is to score each identity class independently, then compare the results. A mature programme usually demonstrates:

• Clear ownership for each identity type, including business and technical approvers.
• Documented policy enforcement for access, rotation, and exception handling.
• Inventory coverage for accounts, keys, certificates, tokens, and workloads.
• Telemetry that shows who or what used an identity, when, and from where.
• Automated revocation or expiration for non-human credentials after the task ends.

For machine identities, the baseline is usually stronger when the organisation adopts workload identity and just-in-time credentialing rather than static secrets. Guidance from NHI Management Group’s Top 10 NHI Issues and the 52 NHI Breaches Analysis shows why rotation lag, excessive privilege, and secret sprawl are recurring maturity failures. That is why frameworks such as the NIST Cybersecurity Framework 2.0 should be translated into identity-specific controls, not treated as a generic compliance checklist.

Security teams should avoid averaging human and machine scores into a single number without context. A high human score can mask a low machine score, especially when CI/CD, SaaS integrations, and cloud workloads are outside normal access review cycles. These controls tend to break down when identities are created outside the IAM toolchain, because ownership and revocation then depend on application teams remembering to clean up after deployment.

Common Variations and Edge Cases

Tighter identity measurement often increases operational overhead, requiring organisations to balance stronger assurance against the cost of inventory, review, and automation. That tradeoff is real, especially in hybrid and multi-cloud estates where identity ownership is distributed and some systems cannot yet support modern workload authentication.

Best practice is evolving for how to score machine identity maturity in edge cases. For example, a legacy application with embedded credentials may warrant a lower maturity score even if compensating controls exist, because static secrets create persistent risk. Similarly, a highly automated platform may look mature on rotation and revocation, but still score poorly if engineers can bypass controls through undocumented exceptions. The Ultimate Guide to NHIs — What are Non-Human Identities is useful here because it frames the identity class as a lifecycle problem, not only an access problem.

There is no universal standard for a perfect maturity score yet. Organisations should therefore compare trend lines over time, separate human from machine results, and require evidence for each score band. That makes the measurement operational rather than aspirational, and it helps reveal whether the programme is actually improving or simply documenting the same gaps more neatly.

Related resources from NHI Mgmt Group

• How should teams measure identity governance maturity across human and non-human identities?
• How should organisations measure identity security maturity across human and non-human identities?
• Why does identity security training matter for machine identities as well as human users?
• How should security teams govern non-human identities at scale?