Governing AI agents as users usually misses the operational reality that they are automated entities acting across systems, not people following a fixed workflow. Treating them as non-human identities forces clearer controls for authentication, authorization, task scoping, logging, and revocation. That model better fits machine-run access and makes it easier to apply least privilege consistently.
Why This Matters for Security Teams
Governing AI agents as users sounds familiar, but it often hides the core risk: an agent can authenticate, call tools, chain actions, and operate at machine speed without human judgment between steps. That makes it a security governance problem, not just an account management problem. The NIST AI Risk Management Framework is useful here because it pushes teams to manage AI risk across the system lifecycle, not only at the login boundary.
The difference matters when an agent can generate its own requests, reuse credentials, or trigger downstream automation. A “user” model tends to focus on onboarding, passwords, and role assignment. A non-human identity model forces a more precise question: what exact machine capability is being granted, for which system, under what conditions, and with what revocation path? That framing is much closer to how modern agents behave across APIs, SaaS platforms, code repositories, and internal workflows.
Security teams also need to distinguish between the agent itself, the model behind it, and the operator who approved its deployment. Current guidance suggests these should not be collapsed into one control owner. In practice, many security teams encounter excessive access and weak audit trails only after an agent has already chained permissions across systems that were never intended to be linked.
How It Works in Practice
Governing AI agents as non-human identities means assigning controls to the agent’s execution identity, not to a human-style persona. The practical objective is to make every action attributable, scoped, and revocable. That includes authentication for the agent, authorization for each tool or API, and logging that records the agent, the action, the context, and the approval path. For agentic systems, the OWASP Top 10 for Agentic Applications 2026 and the MITRE ATLAS adversarial AI threat matrix are helpful for thinking about prompt injection, tool abuse, and adversarial manipulation.
In operational terms, teams usually need:
- A distinct identity for each agent instance or service tier, not one shared account for all agents.
- Short-lived credentials or tokens with narrowly defined tool scopes.
- Policy checks before high-risk actions, such as payments, deletions, or privilege changes.
- Centralised logging that can be correlated in SIEM and reviewed for abuse patterns.
- Fast revocation when an agent changes purpose, drifts from policy, or is decommissioned.
This is where NHI thinking is stronger than user thinking. Non-human identities are designed around machine-to-machine access, lifecycle control, and automated revocation. They also fit better with governance expectations in NIST Cybersecurity Framework 2.0, especially around identity management, logging, and protective controls. For higher-risk deployments, current practice is evolving toward explicit approval gates, provenance checks, and model-output validation before the agent can act on external systems. These controls tend to break down when many agents share one credential namespace because attribution and containment become too weak to support reliable response.
Common Variations and Edge Cases
Tighter identity control often increases operational overhead, requiring organisations to balance least privilege against the speed that makes agents useful. That tradeoff becomes more visible in environments with many short-lived agents, shared tooling, or rapid experimentation.
There is no universal standard for agent identity governance yet, so teams should treat some practices as evolving rather than settled. For example, some organisations bind one identity to one agent, while others bind one identity to a workflow or service class. The right model depends on blast radius, audit needs, and how much autonomy the agent has. Where agents only draft outputs and do not execute actions, a lighter control set may be acceptable. Where agents can modify records, move funds, or change security settings, stronger NHI-style controls are warranted.
The biggest edge case is an agent that looks like a user in the interface but behaves like infrastructure in the backend. That hybrid model can confuse ownership, incident response, and offboarding. The safest approach is to document the agent’s execution scope, map each permitted tool to a control owner, and review whether the identity should be treated as a service principal, an NHI, or a human proxy. That distinction becomes especially important when the agent is connected to external systems through connectors or AI risk governance processes that were designed for static workflows rather than autonomous execution.
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, OWASP Non-Human Identity Top 10 and MITRE ATLAS address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A01 | Agentic attack paths like tool abuse and prompt injection shape this identity model. |
| NIST AI RMF | GOVERN | AI governance requires clear accountability for autonomous system behaviour. |
| OWASP Non-Human Identity Top 10 | NHI-1 | Non-human identities need distinct lifecycle and access controls from users. |
| NIST CSF 2.0 | PR.AA | Identity and access controls underpin safe machine-to-machine authorization. |
| MITRE ATLAS | AML.TA0001 | Adversarial manipulation of models can redirect agent decisions and actions. |
Treat agents as machine identities with scoped credentials, auditability, and revocation.
