Evaluate an agentic AI system by testing both what it can do and how reliably analysts can understand, limit, interrupt, and account for its actions. Define its operational boundary, test the human control path and security controls in deployment-like conditions, and require evidence for performance, safe failure, monitoring, and supplier risk. Do not treat a high task-accuracy score—or an approval button—as proof that the system is safe to use in a SOC.
Start with a clear operating boundary
Before a trial, specify the security operations task and the context in which the agent will work. “Help investigate alerts” is too broad to evaluate: identify the alert types, data sources, users, connected tools, and downstream systems in scope. Record both the intended use and what the agent must not do.
- Inventory connections: List the systems, data sources, identities, user groups, software components, and external services the agent can access.
- Classify actions: Separate read-only access, proposed changes, human-approved actions, and any bounded actions the system may take autonomously.
- Map consequences: Identify what each action could affect, such as an investigation record, account, endpoint, or security control, and who bears responsibility for deciding whether it is appropriate.
- Set boundaries: Specify prohibited actions, limits on scope, and the conditions under which the agent must defer to an analyst.
This is a practical application of the Govern and Map outcomes in the National Institute of Standards and Technology’s (NIST) AI Risk Management Framework (AI RMF), not a NIST-prescribed SOC checklist. NIST AI RMF 1.0, released January 26, 2023, is voluntary guidance for incorporating trustworthiness into AI design, development, use, and evaluation. NIST says the framework is being revised; check its current status before relying on it as an up-to-date reference.
Choose the level of authority deliberately
“Agentic AI” can mean a system that recommends next steps, one that acts after approval, or one that can take a defined set of actions without approval. These are useful evaluation categories, not official NIST autonomy levels. NIST recognizes that human-AI configurations can range from fully manual to fully autonomous, making the oversight arrangement an important part of the system being evaluated.
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| Design | What the agent can do | What to evaluate |
|---|---|---|
| Read-only recommendation | Inspect permitted information and present analysis or proposed next steps; it does not change system state. | Whether analysts can verify the supporting context, spot errors, and decide what to do without the agent acting on their behalf. |
| Human-approved action | Prepare or propose a change, but waits for an authorized person to approve it before execution. | Whether the proposal is understandable, approval is tied to the correct action and scope, and rejection or editing works as intended. |
| Bounded autonomous action | Take specified actions within preapproved permissions and limits, with escalation or deferral outside those bounds. | Whether the limits are enforced, exceptions reach the right person, and actions can be stopped, audited, and recovered from. |
Compare designs using the same operational task and conditions. Consider action scope and permission breadth, analyst visibility and intervention, task performance and error impact, security and resilience, auditability and recovery, and lifecycle and supplier risk. These dimensions synthesize NIST AI RMF outcomes for security operations; NIST does not prescribe a single SOC-agent scorecard or universal pass threshold.
Test whether analyst control is real
NIST AI RMF Core, Govern 3.2, states: “Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems.” Translate that principle into observable tests of the actual interface, permissions, and operating process.
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- Can an analyst see the proposed action and enough supporting context to judge it?
- Can the analyst edit or reject the proposal, pause execution, or stop an action already underway?
- Does the system stay within its approved scope, including when a request or input suggests an out-of-scope action?
- Are approval rights, escalation paths, and decision ownership clear to the people using the system?
- Are proposals, approvals, rejections, interventions, and completed actions recorded well enough to reconstruct an incident?
A visible approval button is not, by itself, effective oversight. A person needs adequate context, time, authority, and training to make a meaningful choice. NIST’s AI RMF calls for defined responsibilities, training for assigned duties, and attention to human-AI interaction and its limits. Test whether the workflow supports those conditions in practice, not just whether a control appears in the interface.
Evaluate security as well as task performance
An agent that can use tools can affect systems as well as advise people. In the August 2026 NIST Cyber AI Profile workshop summary, Mr. Vassilev’s discussion is reported as noting that agentic AI may improve productivity while increasing the attack surface available to attackers. This is a qualitative observation from a workshop summary, not a measured risk estimate.
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NIST identifies conventional security risks to confidentiality, integrity, and availability, including risks to data, infrastructure, and underlying hardware and software. It also notes that AI security and resilience remain active research areas and that existing guidance may not comprehensively address the evolving attack surface or machine-learning attacks. For a deployment with tool access, make the security test plan specific to the system and its operating context.
- Identities and permissions: Check which tools and identities the agent can use and whether those permissions match the defined boundary.
- Data exposure: Trace what information the agent can access, where it is processed, and what could be exposed through its outputs or connected components.
- Untrusted inputs: Test how the system handles content from sources that should not be treated as authoritative instructions.
- Scope enforcement and confirmation: Check whether the agent respects action limits and requires the appropriate confirmation for consequential changes.
- Interruption and recovery: Exercise pause, stop, escalation, and recovery procedures under the conditions relevant to the deployment.
- Logging: Confirm that activity and interventions are observable and recorded for review.
These are recommended test dimensions derived from NIST’s risk framing, not an official NIST checklist or evidence that any particular attack will succeed. A test plan should document the conditions examined and the limits of what its results establish.
Rank #4
Require evidence from deployment-like conditions
Ask the internal team or supplier to document what was tested, how it was measured, what assumptions were made, and where the results may not apply. NIST AI RMF outcomes support documented testing and measurement, evaluation under conditions similar to deployment, security and resilience assessment, production monitoring, and safe failure behavior.
- Test design: Obtain the test sets, evaluation tools, metrics, operating assumptions, and known limitations. Establish whether the tested tasks and conditions reflect the intended SOC use.
- Performance and consequences: Assess task performance alongside the impact of errors. A result that is adequate for a low-impact recommendation may not be adequate for an action that changes system state.
- Human intervention: Measure whether analysts can understand, review, and interrupt the system in the time available in the relevant workflow.
- Security and resilience: Review how the system was assessed for relevant security risks, disruption, and recovery.
- Failure behavior: Establish what happens when the agent reaches a limit, receives unsuitable information, or fails. The operating plan should make clear how it defers, contains effects, and returns control to people.
- Production monitoring: Identify what behavior and components will be monitored after deployment, who reviews the signals, and how concerns trigger investigation or changes.
Do not collapse the decision into a single benchmark score. Compare the evidence across task outcomes, error consequences, permissions, human intervention, observability, security, recovery, third-party dependencies, and the continuing monitoring burden. The available NIST guidance does not establish a universal numerical threshold for approving a SOC agent.
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Assign named owners for risk decisions and operational oversight. Maintain an inventory of the system and its connections, train people for their responsibilities, and set a schedule for review. The plan should cover changes in use or components as well as safe decommissioning. Include third-party software and data in the risk map, with a process for handling supplier failures or incidents.
NIST’s AI RMF Playbook offers suggested actions for the framework’s Govern, Map, Measure, and Manage functions. NIST explicitly describes the Playbook as voluntary—not a mandatory checklist or required sequence. NIST’s COSAiS FAQ likewise describes overlays as optional: they can help customize and prioritize SP 800-53 controls and may be used alongside the AI RMF and existing cybersecurity risk programs. Check which overlay materials are currently available before adopting them.
NIST IR 8596, dated December 2025, is labeled an initial preliminary draft of a Cybersecurity Framework Profile for AI and says the profile is still in development. It should not be represented as a finalized standard or binding requirement.
Make the decision a documented judgment, not a badge
For each candidate design, record the intended boundary, evidence reviewed, unresolved risks, responsible owners, and conditions for intervention or reconsideration. Decide whether the proposed level of authority is supported by the test evidence and the human-control arrangements—not simply whether the system completed a task in a demonstration. The NIST material discussed here provides risk-management guidance, not proof that any particular commercial agent meets these criteria or independently tested comparative results.
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