How should security teams govern AI agents in purple team exercises?

Why This Matters for Security Teams

AI agents in purple team exercises are not passive test tools. They can chain prompts, call tools, and shift context in ways that expose detection gaps, logging blind spots, and privilege handoff weaknesses. Governance has to focus on what the agent is allowed to do at runtime, not just what the exercise plan says on paper. That aligns with the emerging guidance in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework.

NHI Management Group research on AI agents as a new attack surface shows how quickly agents exceed intended scope when controls are weak, which is exactly why purple teams need explicit boundaries. In practice, many security teams discover agent drift only after the exercise has already created unintended persistence, rather than through intentional pre-exercise governance.

How It Works in Practice

The safest pattern is to treat the agent as a governed identity with a scoped mission, not as a reusable script. Before the exercise starts, define the agent’s purpose, permitted tools, target systems, allowed data classes, and stop conditions. Then bind that mission to an ephemeral workload identity and short-lived credentials so the agent cannot keep acting after the test window closes. For agent identity design, current guidance suggests favouring runtime-issued, task-bound access over long-lived secrets, especially where the agent can call multiple systems in sequence.

In mature purple team setups, security teams usually combine control-plane logging, policy-as-code, and explicit handoff rules:

• Issue credentials per exercise phase, then revoke them automatically when the phase ends.
• Require every tool call to be logged with the agent identity, session ID, and operator approval state.
• Use real-time policy evaluation so access decisions reflect current context, not a static role assigned at kickoff.
• Separate “attack emulation” from “identity creation” so any creation or switching of identities requires explicit approval and traceability.

This is where agentic governance differs from traditional red team tooling. Static IAM models assume predictable access paths, but an autonomous agent may discover new tool chains, retry failed actions, or route through helper services that were never in the original plan. That is why the CSA MAESTRO agentic AI threat modeling framework and the OWASP NHI Top 10 both emphasise runtime control, identity discipline, and traceability. These controls tend to break down when the exercise spans shared sandboxes with standing credentials, because the agent can inherit access paths that were never intended for the test.

Common Variations and Edge Cases

Tighter control often increases exercise overhead, requiring organisations to balance realism against the need to prevent accidental persistence or data exposure. In high-fidelity simulations, teams may allow limited identity switching, but only under a documented approval chain and with a fresh audit trail for each handoff. Best practice is evolving here, and there is no universal standard for how much autonomy a purple team agent should have before the exercise stops being safe.

Edge cases usually appear when the agent has access to production-adjacent systems, shared secrets stores, or agent-to-agent orchestration layers. In those environments, even a well-scoped test can expand if a downstream tool trusts the agent more than the exercise controller does. NHI Management Group guidance on lifecycle processes for managing NHIs is relevant because purple team agents still need provisioning, review, revocation, and post-exercise cleanup. For threat context, the AI LLM hijack breach research and the MITRE ATLAS adversarial AI threat matrix both reinforce that autonomous systems can be redirected once they can reach enough tools. Teams should assume the exercise breaks down fastest when identity boundaries are shared with real workloads or when human operators lose visibility into the agent’s live decisions.

Related resources from NHI Mgmt Group

• How should security teams govern AI agents that use OAuth access?
• How should security teams govern AI agents that can access enterprise systems?
• How should security teams govern AI agents that rely on shared runtime credentials?
• How should security teams govern machine identity credentials in agentic AI environments?