Treat it as a delegated identity path, not a simple user session. Security teams should map the human, service account, and agent involved, then monitor the sequence of actions, the tools used, and the systems reached. That lets teams detect when authorised activity drifts into a higher-risk behavioural pattern before containment becomes impossible.
Why This Matters for Security Teams
When AI-assisted work runs under a human’s credentials, the security problem is no longer just “who logged in.” It becomes a delegated identity chain that can move faster, touch more systems, and behave less predictably than a normal user session. That is why teams should think in terms of identity inheritance, not session hygiene.
This is where static IAM models start to fail. A human may approve a task, but the agent or automation using that access can chain tools, call APIs, and reach systems the original user never intended. Current guidance in OWASP Non-Human Identity Top 10 and NIST Cybersecurity Framework 2.0 both point toward stronger identity governance, but AI-assisted work needs tighter runtime controls than a standard workforce account review.
NHIMG research shows the practical danger of weak visibility: only 1.5 out of 10 organisations are highly confident in securing NHIs, and inadequate monitoring remains one of the leading causes of compromise in The State of Non-Human Identity Security. In practice, many security teams encounter abuse only after delegated access has already reached a privileged system, rather than through intentional design.
How It Works in Practice
Security teams should govern AI-assisted work as a three-part path: the human initiator, the inherited credential, and the software entity performing the action. The goal is to preserve accountability without pretending the agent behaves like a person. That means recording the full action sequence, not just the login event.
A practical control model usually includes four elements:
- Map the human, service account, and agent to a single delegated workflow so ownership is clear.
- Issue short-lived access where possible, using just-in-time provisioning rather than reusable standing credentials.
- Prefer workload identity for machine execution, such as cryptographic proof of the workload through standards like NIST SP 800-63 Digital Identity Guidelines when identity assurance is required, and workload-oriented controls for runtime verification.
- Evaluate policy at request time, not only at onboarding, so the system can account for the task, data sensitivity, destination, and tool chain involved.
This is also where secret hygiene becomes a governance issue. Static credentials inherited by an AI workflow can persist far beyond the task that justified them, which is why NHIMG recommends reviewing the difference between long-lived and dynamic secrets in Ultimate Guide to NHIs — Static vs Dynamic Secrets and tightening handling of Secret Sprawl across automation paths.
Security telemetry should also distinguish normal delegation from suspicious escalation. Look for unusual tool chaining, broadening of data access, cross-environment movement, or repeated access to secrets stores. Current best practice is evolving, but the operational principle is consistent: every inherited credential should have a task boundary, a time boundary, and a revocation path. These controls tend to break down in environments with legacy shared accounts and opaque scripting because the true actor cannot be reliably attributed at runtime.
Common Variations and Edge Cases
Tighter delegated-identity controls often increase operational overhead, requiring organisations to balance speed of AI-assisted work against the cost of more frequent approvals, shorter TTLs, and richer logging.
Some environments need special handling. In developer tooling, the agent may only need repository-scoped access for minutes, while in operations workflows the same agent may need broader read access but very limited write capability. In both cases, role-based access alone is usually too coarse because the risk changes with task context. That is why policy-as-code and real-time decisioning are becoming the preferred model, even though there is no universal standard for this yet.
Edge cases also include shared service accounts, break-glass access, and multi-agent pipelines. Those patterns can be legitimate, but they should not inherit a human’s full standing privileges by default. If an agent must act on behalf of a person, the session should carry explicit delegation metadata, short duration, and revocation on completion. For teams building toward this model, the Top 10 NHI Issues page is a useful reminder that monitoring gaps and over-privilege remain the recurring failure modes. Best practice is evolving, but the safest assumption is that a delegated AI workflow will eventually reach a system its author did not anticipate.
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 | AG-03 | Addresses delegated agent actions and runtime authorization risks. |
| CSA MAESTRO | GOV-02 | Covers governance for autonomous workflows using inherited access. |
| NIST AI RMF | Supports governance and accountability for AI-assisted decisions. |
Bind every agent action to task-scoped policy and revoke access when the task ends.
