They should classify it by role, map it to a service account, and review the privilege attached to that identity before applying policy. An inference server, an orchestrator, and a tool runtime do not need the same access, so governance has to follow the discovered function rather than the container alone.
How teams should handle an AI agent once it is found in a cluster
Discovery is only the starting point. The next step is to determine what the agent is actually doing, because the right control depends on whether it is acting as a user-facing assistant, a tool runner, a workflow orchestrator, or a server-side inference component. Treat the discovered workload as an identity-bearing runtime until you can prove otherwise, then align access, ownership, and policy to that role.
That matters because AI agents often share the same cluster patterns but not the same authority. One pod may only score or classify inputs, while another can call APIs, mutate state, or chain actions through tools. If teams apply a single blanket policy to every container, they usually miss the real decision point: who or what the agent can act as, and what it can reach.
A practical discovery workflow starts with function, not image name. Inventory the service account, the upstream caller, the downstream tools, and the external systems the agent can reach. Then decide whether the agent is merely hosting a model, mediating requests, or executing actions on behalf of another principal, because each of those cases requires different containment and different review depth.
Why role classification and identity mapping come before policy
Role classification is what turns discovery into governance. Once you map the agent to a service account and a business function, you can distinguish benign telemetry from real authority and spot when a workload has accumulated privileges it does not need. That is the point where policy becomes meaningful, because policy can only constrain the access that has been identified.
Mapping also helps separate the container from the authority. A runtime that merely serves model output should not inherit the same access pattern as an orchestrator that triggers tools, and a tool runtime should not be given the same privileges as the control plane that schedules it. This is the same least-privilege logic teams already use elsewhere in infrastructure, but here it has to follow the discovered agent role instead of the deployment wrapper.
AI Agent Authorisation Guide is the clearest internal reference for turning that role review into per-action authorization, task-scoped access, and approval gates. For discovery and inventory, Shadow AI and AI Agent Discovery Guide helps teams locate unmanaged agents before they are treated as routine workloads. When the question is how to model the agent itself, Agentic AI Identity Guide is useful because it explains identity, delegation, registration, and retirement as lifecycle decisions rather than one-time deployment choices.
What good governance looks like after the first review
Good governance means the team can answer three questions without guessing: what the agent is allowed to do, which identity it uses to do it, and who owns the decision to change that access. If those answers are unclear, the workload is not ready for policy automation yet, because automated enforcement will simply preserve an already ambiguous authority model.
The same review should also test whether the discovered agent is over-scoped for its function. If an inference server can reach production tools, or if an orchestrator can both schedule and execute privileged actions, the access boundary is too wide. In practice, the safest pattern is to separate observation, planning, and execution so that each step has a narrower identity and a narrower blast radius.
Zero Trust for AI Agents supports that approach by treating each request as something to verify rather than something to inherit by default. AI Agent Observability, Audit and Incident Response Guide becomes relevant once teams need evidence that the agent’s actions are attributable and reversible. For teams that want a broader security model, Agentic AI Security Guide provides the layered view of inputs, tools, orchestration, and identity that helps prevent role confusion.
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 Zero Trust (SP 800-207) sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | The agent's cluster identity can hold excess access for its function. |
| NHI-01 — Improper Offboarding | Discovered agents need ownership and retirement control after classification. | |
| NHI-04 — Insecure Authentication | Mapping the agent to a service account depends on trustworthy authentication. | |
| Recommendation — Reduce assigned permissions to the smallest role the agent actually needs. Revoke access and retire the identity when the agent is no longer needed. Use strong, bound authentication for the agent's runtime identity. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The question is about classifying agent role before granting policy-bound privilege. |
| Recommendation — Constrain the agent's authority to the specific actions its role requires. | ||
| NIST Zero Trust (SP 800-207) | PR.AA-05 — Least Privilege Access | Role-to-policy mapping here is a least-privilege decision for agent access. |
| Recommendation — Enforce least privilege per request and per agent action. | ||
Practitioner Guidance
What to prioritise: Identify the agent’s effective authority before you tune runtime controls. If the service account can reach systems that the declared function does not require, reduce that access first, because the privilege boundary is the real control surface.
What to verify: Confirm the discovered agent has a named owner, a mapped service account, and a documented purpose that matches its actual tool and API reach. If any of those three are missing, treat the agent as an exception rather than a standard deployment.
Decision rule: If the workload can act on behalf of another principal, review it as delegated authority, not as a generic application pod. If it cannot, keep the policy narrower and avoid giving it workflow privileges just because it sits in the same cluster as one that does.
Practitioner takeaway: The cluster is where you find the workload, but the identity is where you decide the risk. Teams should classify first, map authority second, and only then apply policy that matches the agent’s real function.
Related resources from NHI Mgmt Group
- What do teams get wrong when they try to investigate an AI agent incident after the fact?
- Why is single-provider AI agent governance not enough for enterprise security?
- How should security teams handle risks from AI browser extensions?
- How should security teams govern API keys used for generative AI access?