Should organisations govern API keys and service tokens like other non-human identities?

Why This Matters for Security Teams

API keys and service tokens often sit outside the controls applied to user identities, yet they can authorize production workloads, CI/CD pipelines, SaaS integrations, and AI services. That makes them operationally equivalent to non-human identities even when the organisation has not formally classified them that way. The practical risk is not only leakage, but also weak ownership, oversized scope, and credentials that survive long after the system or integration should have been retired.

Security teams frequently underestimate how much blast radius a single machine credential can create. A token embedded in automation may bypass interactive controls, while a long-lived API key can remain valid across code changes, staff turnover, and platform migrations. Guidance from NIST Cybersecurity Framework 2.0 supports this broader governance approach by tying identity, access, and monitoring to risk management rather than to whether a human is present at login.

In practice, many security teams encounter service-token abuse only after an integration outage, a leaked secret, or a suspicious workload call has already exposed the gap in ownership.

How It Works in Practice

Effective governance starts by inventorying every API key, service token, and similar machine credential, then assigning each one a business owner, technical owner, purpose, and expiry or review date. This is where the identity bridge matters: if a credential can authenticate, authorize, and be revoked, it should be managed with the same discipline as other non-human identities, even if it is stored in a secrets manager.

Operational controls usually include scope minimisation, short lifetimes where the platform allows it, rotation automation, and revocation paths that work even during incident response. Secrets should be tied to workload identity where possible, because workload-native authentication reduces dependence on static shared credentials. Logging is also essential. Teams need to know which token was used, by which system, from where, and for what action, so monitoring can support detection and investigation.

• Classify tokens by environment, application, and privilege level.
• Replace hard-coded keys with managed secret distribution and workload identity where feasible.
• Rotate credentials on a schedule and after any suspected exposure.
• Alert on unusual usage patterns, dormant credentials, and privilege drift.
• Revoke credentials when the owning service, pipeline, or integration is decommissioned.

For broader identity governance, the NIST Digital Identity Guidelines are useful as a reference point even though they focus on digital identity proofing and authentication rather than machine credentials specifically. The key takeaway is that assurance should match the risk posed by the credential, not the convenience of issuing it. These controls tend to break down in sprawling multi-cloud and developer-led environments because tokens are created faster than ownership, review, and revocation processes can keep up.

Common Variations and Edge Cases

Tighter credential governance often increases operational overhead, requiring organisations to balance stronger control against deployment speed and automation reliability. That tradeoff is especially visible in legacy systems, third-party integrations, and CI/CD pipelines that were built around static keys. In those environments, full rotation or short-lived credentials may not be immediately feasible, so current guidance suggests prioritising the highest-risk secrets first and phasing improvements over time.

There is no universal standard for this yet, but best practice is evolving toward treating all long-lived machine credentials as inventoryable assets with explicit ownership and lifecycle controls. This matters even more where API keys can invoke privileged admin functions, move data across trust boundaries, or connect to AI services that have tool access. In those cases, the credential is not just a secret, it is an access path with governance implications.

For organisations handling regulated or sensitive data, the monitoring and accountability expectations in NIST Cybersecurity Framework 2.0 should be extended to machine identities through policy, logging, and review cadence. The practical benchmark is simple: if a credential can be used to change state, read data, or trigger actions, it needs an owner and a retirement plan.

Related resources from NHI Mgmt Group

• Should organisations treat certificates and tokens like other non-human identities?
• How should security teams govern non-human identities at scale?
• How should security teams govern non-human identities for compliance?
• How should security teams govern non-human identities in Salesforce?