What should organisations test before adopting agentic AI in security operations?

Organisations should test whether the agent can act safely under failure, whether its actions are traceable, and whether an incorrect decision can be rolled back. The key question is not only what the agent can do, but what happens when upstream telemetry is wrong or incomplete. Without that test, automation can spread error faster than humans can correct it.

Why This Matters for Security Teams

agentic ai changes security operations from recommendation to execution. That matters because the control plane is no longer just a human analyst and a ticketing workflow; it can include autonomous actions that close cases, quarantine assets, rotate secrets, or trigger containment. The operational risk is not limited to model error. It also includes prompt injection, tool abuse, stale telemetry, and overbroad permissions, all of which are highlighted in the OWASP NHI Top 10 and the NIST AI Risk Management Framework.

NHI Management Group research shows why this cannot be treated as a narrow automation question. In AI Agents: The New Attack Surface report, 80% of organisations reported AI agents had already performed actions beyond their intended scope, including access to unauthorised systems and disclosure of credentials. That is a governance failure as much as a technical one: if the agent can act, then its scope, logging, and rollback path must be tested before production use. In practice, many security teams encounter failure only after an agent has already amplified a bad alert or taken a harmful action, rather than through intentional pre-deployment validation.

How It Works in Practice

Testing agentic AI in security operations should look like operational resilience testing, not model benchmarking. The central questions are whether the agent can be constrained, observed, and safely reversed when its inputs are wrong. Current guidance from OWASP Agentic AI Top 10 and MITRE ATLAS adversarial AI threat matrix suggests testing should cover both misuse by attackers and accidental misuse by the agent itself.

• Run failure-mode drills with poisoned or incomplete telemetry to see whether the agent escalates incorrectly.
• Verify tool permissions, especially for ticketing, SOAR actions, EDR containment, and secret rotation.
• Test traceability end to end: prompt, retrieval context, decision, tool call, and human approval if one exists.
• Confirm that every destructive action has a rollback, compensating control, or human veto path.
• Stress test identity and secret boundaries so the agent cannot inherit standing privilege beyond the task.

This is where agentic AI intersects with NHI governance. If the system uses service accounts, API keys, or delegated tokens, the question is not just whether the model is accurate. It is whether those non-human identities are scoped, rotated, and monitored so an error does not become a lateral-movement event. NHIMG’s analysis of CoPhish OAuth Token Theft via Copilot Studio shows how quickly agent workflows can become identity attack paths when tokens are exposed or over-permissioned.

Testing should also include adversarial inputs that try to induce unsafe actions, not just broken data feeds. Where agents can browse, call APIs, or summarise investigations, prompt injection and retrieval poisoning should be part of the test plan. These controls tend to break down when a security team connects the agent to production incident response tooling without isolating credentials, approvals, and blast radius.

Common Variations and Edge Cases

Tighter control often increases latency and analyst workload, so organisations have to balance speed against assurance. That tradeoff is especially visible in high-volume SOC environments, where full human approval for every step can make the system unusable, but full autonomy can create irreversible mistakes. There is no universal standard for this yet, so current guidance suggests tiering actions by risk rather than treating all agent steps the same.

Low-risk actions, such as drafting an incident summary or enriching a case, can tolerate looser guardrails. High-impact actions, such as disabling accounts, quarantining hosts, or changing firewall policy, should require stronger verification, explicit approval, and immutable audit logging. For teams evaluating the broader attack surface, NHIMG’s AI Agents: The New Attack Surface report is a useful reminder that visibility gaps are common: many organisations still cannot fully track what agents access or change.

Edge cases also matter. Agents that depend on third-party plugins, RAG pipelines, or shared service identities need separate testing because failures often emerge at the integration layer rather than in the model itself. This is especially true in environments with incomplete telemetry, fragmented asset inventories, or mixed human and machine approvals. In those environments, the guidance breaks down because the agent is being asked to make security decisions faster than the organisation can validate the data it is using.

Related resources from NHI Mgmt Group

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