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Before an AI feature ships, define its intended use and accountable owner, evaluate the complete feature in representative conditions, verify risk-based security and privacy controls, document unresolved risks, and establish monitoring and incident response. Use the checklist below as a decision aid—not as a guarantee of safety or compliance—and adapt it to the feature’s users, impact, integrations, and failure consequences.
What a ship gate should decide
A ship gate is a documented release decision: whether the feature is ready for its intended context, what limitations apply, who accepts any remaining risk, and how the team will detect and respond to problems. It should evaluate the AI-enabled product as people will use it, not just a model in isolation.
Two useful resources address different parts of that work. NIST’s voluntary AI Risk Management Framework (AI RMF) provides a broader approach to governing, mapping, measuring, and managing risk. The OWASP AI Security Verification Standard (AISVS) provides testable security requirements for AI applications. They complement each other; neither is a substitute for deciding what risks matter in your product.
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1. Define purpose, boundaries, and ownership
Write down the task the feature supports, who will use it, and the setting in which it is intended to work. Specify what is outside scope and what could happen if the system is wrong, unavailable, or used in a different context. NIST’s AI RMF Core calls for defining specific tasks and methods and documenting limits on generalizability.
- Purpose: What user task does the feature support, and what decisions or actions may rely on its output?
- Users and setting: Who interacts with it, and under what operating conditions?
- Boundaries: What uses are unsupported, and how will users learn about those limits?
- Human control: When is review, override, deferral, or a stop condition required?
- Accountability: Who owns the release decision and the associated risks?
NIST’s AI RMF assigns leadership responsibility for AI-related risk decisions through its Govern function, while Map addresses the system’s tasks and context. A release owner should be able to explain why the feature is suitable for its intended use, not simply report that tests passed.
2. Evaluate the complete AI-enabled system
Build the evaluation around the actual product path: application code, input and output handling, data, model, prompts, integrations, tools, deployment configuration, and human-AI workflow. A model-level test cannot establish how the deployed feature behaves when its surrounding components or users affect the result. NIST’s Generative AI Profile highlights risks from third-party integrations, and OWASP AISVS addresses AI-enabled applications across their lifecycle.
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Make the evaluation representative and repeatable
Define test cases that reflect intended users, inputs, and operating conditions. Choose measures that match the task; record uncertainty, limitations, and the conditions under which results may not generalize. Keep the test method, results, and accountable reviewer with the release decision. Consider independent review where the impact or uncertainty warrants it.
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- Are test cases representative of the people, inputs, and conditions the feature will encounter?
- Do the metrics measure success and meaningful failure for the intended task?
- Are validity and reliability supported for the intended context, with uncertainty and limitations documented?
- Have safety, security and resilience, privacy, transparency, and accountability been assessed against mapped risks?
- Can another reviewer understand and repeat the evaluation?
NIST’s AI RMF Core says, “AI systems should be tested before their deployment and regularly while in operation.” It calls for documented evaluation of validity and reliability, safety, security and resilience, privacy, and transparency and accountability. The specific measures depend on the system’s context and the risks identified; a checklist does not supply a universal pass score.
3. Map data, integrations, and suppliers
Trace information through the feature, from user input to model or tool calls, storage, logs, and outputs. Identify what data enters the system, where it goes, who can access it, and how long it is retained. For third-party models, tools, or generated data, assess additional privacy, intellectual-property, and information-security exposure.
- What information is sent to each model, service, or connected tool?
- What is retained, by whom, and for how long—including logs and derived data?
- Could a third-party component expose sensitive inputs or return untrusted content that affects downstream actions?
- Has supplier and acquisition due diligence been matched to the service and procurement context?
- Would a software bill of materials, service-level agreement, or attestation report help establish transparency and responsibility?
NIST’s Generative AI Profile identifies these as possible approaches to third-party risk, not mandatory artifacts for every deployment. Select controls according to the system, supplier relationship, and data involved.
4. Verify security and privacy controls
Start with the risks identified for this feature, then select security and privacy requirements that address them. For security verification, OWASP AISVS offers a vendor-neutral catalogue of verifiable, testable, implementable requirements spanning areas such as training data, model development, deployment, agent orchestration, monitoring, and retirement. Use applicable requirements as evidence-producing tests alongside broader risk management—not as a requirement to implement every item regardless of context.
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OWASP Foundation describes AISVS 1.0, released in June 2026, as containing 191 requirements across 12 chapters and three appendices. That count describes the catalogue’s scope; it does not establish that every requirement applies to every feature or that following the standard guarantees an improved outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Record the decision and prepare for operation
Before release, document the evidence reviewed, known limitations, residual risks, and the person or group accepting those risks. Decide what signals require human escalation, rollback, shutdown, or incident response, and assign responsibility for monitoring. NIST’s AI RMF Core expects safety and resilience evaluation, including failure behavior and response; its Generative AI Profile identifies monitoring and incident response as relevant practices.
- What risks remain, and are they within the organization’s risk tolerance?
- Which observed behaviors or operational signals trigger escalation, rollback, shutdown, or incident response?
- Who monitors the feature, and who has authority to intervene?
- How will the team review changes to the model, data, prompts, tools, integrations, or operating context?
- What test evidence and release records must be retained so the decision can be revisited?
Re-evaluate after launch as the system or its context changes. Testing is not a one-time pre-deployment event: operational monitoring and regular assessment help determine whether the original assumptions and release decision still hold.
How to use the checklist without treating it as a guarantee
Set the depth of review according to the feature’s intended users, potential impact, integration pattern, and consequences of failure. A low-impact assistive feature and a system that can trigger consequential actions do not automatically need identical evidence or controls. The release record should make the chosen scope and remaining uncertainty visible.
NIST’s AI RMF is voluntary, and its actions are meant to be adapted to context. The sources cited here do not establish that a particular checklist reduces incidents or improves AI performance by a specific amount. The practical value of a ship gate is the disciplined decision it enables: documented evidence, explicit ownership, and a plan for detecting and managing risk after release.
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