Treat them as separate actor classes with shared governance patterns. Both need ownership, least privilege, lifecycle controls, and revocation discipline, but AI-connected identities also need tighter runtime oversight because their behaviour can change through delegated tools and sessions. A single governance model should unify inventory, access review, and offboarding while keeping control rules specific to each identity type.
Why This Matters for Security Teams
Machine identities and AI-connected identities are often managed in the same IAM stack, but they do not behave the same way. A service account usually follows a known execution path, while an AI agent can change actions based on prompts, tool access, and session context. That difference matters because control failures can spread quickly when identity is also the path to secrets, APIs, and infrastructure.
Current guidance suggests treating the programme as a shared governance layer with separate enforcement logic. The shared layer should cover ownership, inventory, review cadence, and revocation, while the control layer should reflect whether the actor is a fixed workload or an autonomous system. That distinction aligns well with the NIST Cybersecurity Framework 2.0 emphasis on continuous governance, and with NHIMG research showing that lack of credential rotation is cited as the top cause of NHI-related attacks by 45% of organisations in The State of Non-Human Identity Security.
Security teams usually get this wrong by forcing both identity classes into one review model, then discovering that “approved access” for a machine token does not mean safe runtime behaviour for an agent. In practice, many security teams encounter misuse only after an exposed credential is already being abused, rather than through intentional governance.
How It Works in Practice
A workable programme starts by classifying identities by actor type, not by where they live. Machine identities include workloads, CI/CD runners, integrations, and API clients. AI-connected identities include agents, orchestration services, tool-using LLM applications, and delegated assistant sessions. Both need a single inventory, but they should not share identical policy rules.
For machine identities, the baseline remains familiar: least privilege, short-lived secrets where possible, regular rotation, and automated offboarding when a workload is retired. For AI identities, the IAM model needs runtime awareness. A prompt-driven agent may request a tool it has never used before, chain multiple actions, or escalate through delegated permissions. That is why best practice is evolving toward intent-based or context-aware authorization, where decisions are evaluated at request time rather than solely at provisioning time. Frameworks such as NIST AI Risk Management Framework and NIST CSF 2.0 support that shift by tying governance to risk, accountability, and continuous control.
Operationally, security teams should combine four control moves:
- Use a central ownership register for both actor classes, with a named business or technical owner.
- Issue just-in-time, short-lived credentials for sensitive actions instead of relying on standing access.
- Bind workloads and agents to cryptographic workload identity where possible, so the system proves what it is at runtime.
- Evaluate policy with context, including task, tool, environment, and data sensitivity, rather than only group membership.
NHIMG’s Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs is useful here because lifecycle discipline is the common control surface, even when the runtime enforcement differs. These controls tend to break down when agentic systems can spawn sub-agents or call external tools outside the IAM boundary because the approval trail no longer matches the real execution path.
Common Variations and Edge Cases
Tighter control often increases operational overhead, requiring organisations to balance strong containment against developer velocity and automation reliability. That tradeoff is especially visible when machine identities are embedded in high-frequency pipelines or when AI agents need broad tool access to remain useful.
There is no universal standard for this yet, so the right pattern depends on how much autonomy the identity has. A batch job with a narrow API scope can often be governed with conventional machine-identity rules. A customer-facing agent, by contrast, usually needs layered controls: runtime authorization, session limits, tool allowlists, and stronger revocation discipline. That is why current guidance recommends separating policy logic while sharing governance records, rather than forcing every identity through one generic approval path.
Edge cases also appear when one identity type impersonates another. For example, an agent may use a service account as a backend execution identity, which means the real risk is not just credential theft but delegated misuse. In those cases, teams should review whether the service account has excessive entitlements or whether the agent should be re-architected to use The State of Non-Human Identity Security style controls on rotation, logging, and visibility. The practical rule is simple: unify inventory and oversight, but never assume the same access pattern, trust boundary, or revoke trigger applies to both classes.
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 and OWASP Agentic AI Top 10 address the attack and risk surface, while 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 | Rotation and revocation discipline are core to shared NHI governance. |
| OWASP Agentic AI Top 10 | A-04 | Agentic identities need runtime controls beyond static IAM reviews. |
| NIST AI RMF | AI RMF supports risk-based governance for autonomous systems. |
Automate short-lived credentials and verify every non-human identity has an owner, expiry, and revocation path.
