Use one authorization policy model across all three actor types, but vary the inputs by actor and context. That keeps lifecycle events, delegation chains, and runtime conditions in the same governance path while avoiding separate rule sets that drift over time.
Why This Matters for Security Teams
When token issuance has to serve humans, service accounts, and AI agents, the real risk is not the login ceremony. It is policy drift, inconsistent revocation, and unclear accountability once a token is handed out. A single issuer can still create three very different trust problems: a human session, a machine workload, and an autonomous agent with tool access. Guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point to the same operational truth: identity controls must follow the actor’s behavior, not just the actor’s label.
This is especially important because secrets and tokens now fail outside classic code paths. NHIMG research in The State of Secrets Sprawl 2026 shows that AI-related credential leaks surged 81.5% year-over-year in 2025, which means the issuance layer is now part of the exposure surface, not just the access layer. In practice, many security teams discover inconsistent token rules only after an agent has already chained tools or a service account has been overprovisioned for months.
How It Works in Practice
The best pattern is a single authorization model with different runtime inputs for each actor type. Humans bring strong interactive signals such as device posture, session risk, and step-up authentication. Service accounts bring workload identity, bounded scopes, and lifecycle automation. AI agents bring task intent, tool context, execution policy, and strict runtime constraints. The point is not to create three policy engines. The point is to evaluate one policy language against different evidence.
For machine and agent workloads, current guidance suggests using workload identity as the cryptographic root of trust. That can mean SPIFFE, OIDC-based workload tokens, or equivalent attestation-backed identities, with short-lived tokens issued per task rather than long-lived static secrets. This aligns with CSA MAESTRO agentic AI threat modeling framework, which emphasizes runtime context and tool mediation, and with NHIMG’s OWASP NHI Top 10, which treats uncontrolled secret persistence as a recurring failure mode.
- Use one policy engine, not one policy per actor class.
- Bind human tokens to session context and reauthentication triggers.
- Bind service account tokens to workload identity, narrow scopes, and automated revocation.
- Bind agent tokens to task intent, tool allowlists, and per-action evaluation.
- Prefer short TTLs and automatic renewal only when the runtime context still justifies access.
Policy-as-code is the practical control point here. Real-time evaluation with tools such as OPA or Cedar can decide whether a given request is acceptable at that moment, based on actor type and context. These controls tend to break down when legacy apps require shared bearer tokens across batch jobs, interactive users, and autonomous agents because the application cannot distinguish who is actually acting.
Common Variations and Edge Cases
Tighter token governance often increases integration overhead, requiring organisations to balance faster onboarding against stronger runtime assurance. That tradeoff becomes visible in mixed estates where older applications cannot consume workload identity natively, or where human delegation chains and agent delegation chains are both present. In those environments, best practice is evolving rather than settled, and there is no universal standard for how deeply agent intent should be embedded in the token itself versus enforced at the policy layer.
Two edge cases matter most. First, hybrid delegation, where a human authorises an action that an agent executes later, needs explicit provenance so the original approval is not lost. Second, shared service identities used by multiple jobs should be treated as technical debt, because they blur ownership and make revocation noisy. The operational lesson from LLMjacking: How Attackers Hijack AI Using Compromised NHIs is that exposed credentials are often abused within minutes, so short-lived issuance is not just cleaner design, it is containment.
Where this guidance often fails is in environments that still rely on static bearer tokens embedded in automation scripts, because the issuer cannot reliably distinguish human intent from machine execution after the token leaves the gateway.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO 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 Agentic AI Top 10 | A2 | Covers agent misuse of tools and credentials under runtime conditions. |
| CSA MAESTRO | M1 | Focuses on agentic identity, policy, and runtime control boundaries. |
| NIST AI RMF | Governance and measurement apply directly to mixed human and AI token issuance. |
Evaluate each agent request at runtime with tool-specific allowlists and short-lived credentials.
Related resources from NHI Mgmt Group
- How can organisations govern AI agents that use service accounts and tokens?
- How should security teams govern AI agents that can reset accounts or change credentials?
- Why do AI agents increase non-human identity risk in existing IAM programmes?
- How should security teams manage permissions for AI agents?
