They should issue short-lived delegated tokens, enforce downstream validation of the user context, and revoke credentials when the task ends. The goal is to keep access tied to the session and the request, not to leave the agent with a persistent identity that can be reused later.
Why This Matters for Security Teams
AI agent access that persists after the user session ends turns a temporary task into a reusable foothold. That is a different risk than ordinary privileged access, because the agent can continue acting with borrowed authority long after the human has disconnected. Current guidance from OWASP Agentic AI Top 10 and the CSA MAESTRO agentic AI threat modeling framework points to runtime control, not static entitlement sprawl, as the right model for autonomous systems.
That matters because agents are goal-driven. They chain tools, retry actions, and sometimes continue executing after the original request is no longer active. A token that survives the user session can become a durable credential for unintended workflows, data access, or lateral movement. NHI governance is especially relevant here, and the same pattern is visible in NHIMG research on agent abuse and credential exposure, including AI LLM hijack breach and the broader OWASP NHI Top 10.
In practice, many security teams discover the session boundary problem only after an agent has already reused stale access in a later workflow, rather than through intentional testing of token lifetime and revocation paths.
How It Works in Practice
The practical answer is to bind the agent to the task and the user context at runtime. Issue just-in-time, short-lived delegated credentials for a single request or workflow step, then revoke or expire them as soon as the task completes. That is usually paired with workload identity so the platform can prove what the agent is, not just what secret it possesses. Standards-oriented implementations often use SPIFFE-style workload identities or OIDC-backed tokens, while policy engines evaluate whether the current action still matches the user’s intent and the approved context.
Security teams should design for three checks on every sensitive action: the user who initiated the task, the agent workload performing it, and the policy state at that moment. That means downstream systems should not trust a token simply because it was minted earlier in the session. They should revalidate scope, time, purpose, and target resource before accepting any action. This aligns with NIST AI Risk Management Framework expectations around governability and traceability, and with OWASP Non-Human Identity Top 10 guidance on credential lifecycle control.
- Use per-task tokens with a narrow TTL, not reusable session credentials.
- Enforce intent-based authorisation at request time, not only at login time.
- Invalidate delegated secrets when the user session, task, or approval chain ends.
- Log the originating user, the agent identity, and the downstream action together for audit.
NHIMG research on exposed NHIs shows why speed matters: credential abuse can begin quickly after exposure, so lingering agent tokens create an obvious attack window. These controls tend to break down in long-running batch automations and multi-step orchestration pipelines because no single service reliably owns revocation across every hop.
Common Variations and Edge Cases
Tighter session-bound controls often increase orchestration overhead, so organisations must balance stronger containment against workflow friction. There is no universal standard for this yet, especially where agents hand off work across multiple services, tenants, or human approvers.
In customer support or code-assist environments, the agent may need to preserve state without preserving authority. In those cases, keep conversation memory separate from authorization, and reissue credentials only when the next action is explicitly approved. This is where Ultimate Guide to NHIs and Ultimate Guide to NHIs — Key Challenges and Risks are useful for framing the difference between identity persistence and privilege persistence. Best practice is evolving toward zero standing privilege, runtime policy checks, and revocation hooks that fire when the user stops supervising the task.
For higher-risk deployments, organisations should also align the design with OWASP Top 10 for Agentic Applications 2026 and continue using CSA MAESTRO agentic AI threat modeling framework to test whether the control plane still behaves safely when the agent retries, escalates, or resumes after interruption.
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 | Agent tokens that outlive sessions are an authorization and scope problem. |
| CSA MAESTRO | A3 | MAESTRO maps runtime policy and agent lifecycle controls to real workflows. |
| NIST AI RMF | AI RMF governs accountability, traceability, and control of autonomous AI behavior. |
Define ownership, logging, and revocation controls for agent actions under AI RMF governance.
