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1. Define the feature’s purpose, owner, and boundaries
Start with the use case, not the model. Describe the task the AI feature supports, its intended users and operating setting, and the limits of its intended use. Be explicit about what is out of scope and what a user or downstream system should do when the output is wrong, incomplete, or outside those limits.
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- Task and context: What decision or workflow does the feature support? In what settings and for which users is it intended?
- Failure consequences: What could happen if the system produces an incorrect, misleading, delayed, or unavailable output?
- Human role: Where is review, override, or confirmation required? When should the feature defer or stop instead of answering?
- Accountability: Who owns the release decision, the associated risks, and any later changes?
NIST’s AI Risk Management Framework (AI RMF) places risk ownership and leadership responsibility within its Govern function, and asks teams to define system tasks and context in its Map function. The framework is voluntary; its guidance is not a universal release procedure. NIST AI RMF overview.
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2. Evaluate the complete AI-enabled system
A model score alone is not a ship decision. Test the model in the feature as users will encounter it: with its application, data flows, integrations, tools, deployment configuration, and human-AI workflow. A model that performs acceptably in isolation can behave differently when retrieval, prompts, permissions, service failures, or user actions shape its inputs and outputs.
Build a repeatable evaluation plan before interpreting results. The cases should represent the feature’s intended users, inputs, and operating conditions, including relevant edge cases and foreseeable misuse. Select measures that reflect the actual task; record uncertainty, limitations, and the conditions under which results were obtained. Keep the evaluation evidence with the release decision and assign a reviewer accountable for assessing it. Consider independent review where the feature’s impact or complexity warrants it.
- Assess validity and reliability for the stated use, not for an undefined general capability.
- Evaluate safety, security and resilience, privacy, transparency, and accountability in light of the risks identified for this feature.
- Record test methods, representative conditions, metrics, results, limitations, and review.
- Include what happens during failure: degraded dependencies, unavailable services, unexpected inputs, or outputs that require escalation.
NIST’s AI RMF Core calls for objective, repeatable or scalable testing and documented evaluations. It states: “AI systems should be tested before their deployment and regularly while in operation.” The Core’s measures are contextual; choose them according to mapped risks and intended use. NIST AI RMF Core.
3. Map data, integrations, and supplier responsibilities
Trace information through the feature: what enters it, which models or services receive it, where outputs go, and how long data is retained. Include third-party models, tools, generated data, and connected services in the review. Their involvement can introduce additional privacy, intellectual-property, and information-security risks.
- Document data categories, destinations, access, retention, and deletion practices relevant to the feature.
- Identify external models, tools, and service dependencies, including what they can access and what they return.
- Complete supplier and acquisition due diligence appropriate to the system and procurement context.
- Consider whether a software bill of materials, service-level agreement, or supplier attestation would clarify transparency, responsibilities, or operational expectations.
NIST’s Generative AI Profile identifies third-party risk and these kinds of practices as considerations, not mandatory controls for every system. Select them based on the actual feature and procurement arrangement. NIST published the profile on July 26, 2024. NIST Generative AI Profile.
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4. Verify security with testable requirements
Translate the security risks identified for the feature into requirements that can be tested, then retain evidence of the tests and findings. For an AI-enabled application, the review may need to span training data, model development and deployment, agent orchestration, monitoring, and retirement—not just the interface or model endpoint.
The OWASP AI Security Verification Standard (AISVS) is a vendor-neutral catalogue of verifiable security requirements for AI applications. Version 1.0, released in June 2026, contains 191 requirements across 12 chapters and three appendices. That count describes the standard’s scope; it does not mean every team must implement every requirement. Use the edition and requirements that fit the feature’s architecture and risk, and confirm the current edition when applying the standard. AISVS complements broader risk management; it does not replace it. OWASP AI Security Verification Standard.
5. Make a documented release decision
Bring the purpose, evaluation results, data and supplier review, and security evidence together in one release record. Identify unresolved risks and decide whether they fall within the organization’s risk tolerance. Record who accepts each material residual risk and why; an unresolved issue should not disappear simply because a launch date is near.
- State the release decision and accountable approver.
- List known limitations and residual risks, including their potential consequences.
- Identify any required mitigations, owners, and conditions for release.
- Retain the evidence and rationale so the decision can be revisited as the feature changes.
6. Prepare monitoring, response, and change control
A ship gate is the start of operational oversight, not the end of evaluation. Assign an owner to monitor the feature and define how the team will detect changes in behavior, data, integrations, or operating context. Establish clear triggers for human escalation, rollback, shutdown, or incident response, along with the people responsible for acting on them.
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Review changes to models, data, prompts, tools, configuration, and intended use according to their risk. A material change may invalidate prior evaluation evidence and require new tests before or after release. NIST’s GenAI Profile highlights monitoring and incident response, while the AI RMF Core calls for regular testing in operation and evaluation of safety, including failure behavior and response. NIST Generative AI Profile and NIST AI RMF Core.
How to adapt the gate to your feature
Use the same questions, but scale the depth of evidence and review to the feature’s users, potential impact, integration pattern, and failure consequences. A useful readiness review has four dimensions:
- Coverage: Does it address governance and context, behavior, application security, data and privacy, suppliers, and operations?
- Evidence quality: Are cases representative and tests repeatable? Are metrics, uncertainty, limitations, and review documented?
- Risk fit: Do the controls match who uses the feature, what it can affect, how it is integrated, and what failure means?
- Lifecycle reach: Does the approach cover release as well as monitoring, incident response, and change management?
NIST’s AI RMF Core supports risk mapping, measurement, documentation, and regular testing. OWASP AISVS provides testable AI application security requirements. They address complementary needs rather than serving as interchangeable checklists. Neither framework makes a release automatically safe or compliant, and no outcome statistic establishes that one particular checklist reduces incidents or improves AI performance.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteNIST has said its AI RMF 1.0 is being revised. Treat its recommendations as voluntary guidance and check the current materials when setting your organization’s gate. NIST AI RMF overview and status.
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