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Do not let a prediction authorize an action. Let the model provide evidence the agent can use to propose a next step, then have a separate, deterministic control check the actor’s authority, the exact tool and target, the requested parameters, and any required human approval before execution. Keep permissions narrow and make the system fail closed when a safety check cannot be completed.
Use a separate control between the agent’s plan and the action
A prediction can be wrong, out of date, or outside the model’s intended use. Even a well-calibrated prediction does not establish that a user is authorized to act, that the action is allowed, or that the proposed target and parameters are safe. The agent may interpret the result and suggest an action; a separate policy gate should decide whether that exact action may proceed.
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This division follows OWASP’s recommendation to separate decision-making from execution, with authorization and approval checks in the execution component. It is a practical architecture pattern, not a reference design certified by NIST or OWASP. OWASP AI Agent Security Cheat Sheet
Give each component a limited job
| Component | What it does | What it must not decide alone |
|---|---|---|
| Predictive model | Produces an estimate, classification, score, or other prediction for its intended task. | Whether an action is authorized or should be executed. |
| Typed prediction record | Carries the output together with its meaning, source, version, timing, scope, and uncertainty semantics. | Permission to call a tool. |
| Agent planner | Uses the prediction and other permitted context to propose a tool call or a human review. | Granting itself new privileges or bypassing policy. |
| Independent policy gate | Checks identity, authorization, tool and target allowlists, parameters, approval, and operational limits. | Executing an action without passing the checks. |
| Execution service or tool | Performs only the operation authorized by the policy gate. | Accepting an unvalidated request directly from the model or agent. |
The policy gate should be independent of the model’s answer and the agent’s interpretation. The gate can be deterministic software, an existing authorization service, or another control with clear ownership; the essential property is that model-generated content cannot override it.
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Preserve the prediction’s meaning and limits
Pass model output in a structured record rather than an unlabelled string. NIST’s AI Risk Management Framework calls for documenting system knowledge limits, how outputs may be used and overseen, and interpreting outputs in context. A practical record can carry the following fields; this is an implementation recommendation, not a NIST-prescribed schema. NIST AI RMF Core
- Output and meaning: identify whether the value is a class, score, estimate, or other output, and define what it means.
- Provenance: identify the model and version, and enough source or input context to determine what the prediction refers to.
- Time and scope: include when the prediction was produced and the entity, event, or interval it covers. Reject records that are stale or out of scope for the requested action.
- Uncertainty semantics: explain what any confidence, probability, or uncertainty field represents. Do not treat an undocumented score as a calibrated probability or as an authorization threshold.
- Known limits: make relevant assumptions and intended-use boundaries available to the planner and policy gate.
Define how missing, malformed, contradictory, stale, or out-of-scope records are handled. A safe default is to stop the action path and ask for an appropriate review or return a non-actionable result—not to infer the missing context.
Build the authorization and execution path
Use this sequence as a starting point: predictive model → typed prediction record → agent planning → independent policy gate → execution service or tool. The agent proposes; the gate authorizes; the execution service acts.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Define the task and boundaries. State the intended use, expected benefits and harms, prohibited actions, knowledge limits, and who is responsible for oversight. Decide whether the proposed system should be used for this task at all. NIST’s framework organizes this risk work around governing, mapping, measuring, and managing AI risks. NIST AI RMF Core
- Limit available tools and credentials. Allowlist the tools the agent needs and give them the least privilege that will support the task. Do not give the agent broad credentials and expect its prompt or the prediction to enforce access boundaries. OWASP AI Agent Security Cheat Sheet
- Validate the proposed call. Parse the agent’s structured output and reject unknown tool names, malformed requests, unauthorized targets, and parameters outside the approved scope. Validate before showing a request for approval and again at the execution boundary, so later changes cannot turn an approved request into a different action.
- Apply policy and operational limits. Check the authenticated caller’s authority, the requested operation and resource, and any action, rate, or retry limits. Do not use a model prediction or the agent’s explanation as proof of authorization.
- Require approval when the action warrants it. For consequential actions, obtain explicit human approval before execution. Bind approval to the actor, tool, resource, normalized parameters, timestamp, and expiry; use stronger authentication or replay protection where appropriate. An approval for one action should not become blanket permission for later calls.
- Execute only the approved request. The execution service should accept the exact validated request and refuse changes to its tool, target, or parameters after approval.
- Record the decision and observe the system. Log structured policy decisions and approval metadata, while protecting secrets and sensitive data. Define safe behavior if required audit logging is unavailable.
Match oversight to the consequence of failure
Not every tool call needs the same review path. Set oversight according to the potential impact, reversibility, and scope of the action. OWASP recommends human review for high-risk actions and treats an unmapped tool as high risk in its example; NIST likewise calls for defined human-AI oversight roles. Neither guidance supplies a universal approval threshold for every industry or use case. OWASP AI Agent Security Cheat Sheet NIST AI RMF Core
For a high-impact request, show the reviewer the exact action, target, parameters, and relevant prediction context. The reviewer should be able to reject the action; the system should not convert silence, a timeout, or an unavailable approver into approval. An unmapped tool or a request the policy cannot classify should be treated as requiring review or blocked, not implicitly trusted.
Fail safely when a control or assumption breaks
Set explicit failure behavior for the prediction, policy service, approval path, and execution layer. In particular, stop rather than proceed if a tool is unknown, a policy lookup fails, approval is missing or invalid, or a required audit record cannot be created. Define recovery steps for operators so that a temporary failure does not lead to bypassing controls.
NIST AI RMF 1.0, Measure 2.6, says: “The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits.” This is a framework outcome, not a guarantee that a particular integration is safe. NIST AI RMF Core
Test the whole chain, then reassess changes
Evaluate more than predictive accuracy. Test whether the complete workflow rejects unsafe calls and handles the operating conditions it is expected to encounter. Include cases such as invalid or stale predictions, missing context, uncertain outputs, adversarial inputs, unknown tools, out-of-scope parameters, expired approvals, and unavailable policy or logging services. Test the behavior of the agent and tools under conditions similar to deployment, not just the model in isolation.
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- Measure whether outputs are valid for the intended task and whether limitations are understood in context.
- Check that authorization, parameter validation, approval binding, and least-privilege controls cannot be bypassed through alternate call paths.
- Monitor component behavior in production, track risks over time, and review whether the system continues to meet its defined risk tolerance.
- Exercise incident response and recovery, including how operators disable the action path and investigate a failure.
- Repeat relevant adversarial testing and risk assessment after changes to the model, agent instructions, tools, retrieval inputs, or operating context.
NIST AI RMF 1.0 was released on January 26, 2023, and NIST describes it as voluntary and says it is being revised. NIST’s security and resilience work also notes that AI security remains an active research area; planned control overlays for predictive AI and agent systems should not be mistaken for completed guidance. Treat the framework as guidance, not certification or proof of compliance, and verify current status and any sector-specific or legal requirements before making compliance claims. NIST AI Risk Management Framework overview NIST AI RMF FAQs NIST AI security and resilience
Evaluate an implementation by its control boundaries
When comparing designs, focus on where authority resides rather than on whether a model is described as confident or capable. Ask:
- Which component has final authority to execute, and is that authority separate from the model and agent?
- Are tools and credentials narrowly scoped, with tool names, targets, and parameters independently validated?
- Does human review scale with impact and reversibility, and is approval attached to the exact request?
- What happens if prediction, policy, approval, execution, or logging is unavailable?
- What evaluation, monitoring, incident-response, and audit evidence shows that the full chain behaves as intended?
There is no universal confidence cutoff or approval threshold in the cited guidance. Set any thresholds against the domain, action, jurisdiction, and organizational risk tolerance; do not substitute a model score for a permission check.
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