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Assess an AI platform against the specific work you plan to give it—not a vendor’s general promise that it is “safe.” Define the tasks, users, data, affected people, permissions and possible consequences; then ask for current evidence that relevant controls are implemented, evaluate the platform in your own configuration, document what risk remains, and assign ongoing oversight.
The National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF 1.0) and its Generative AI Profile can help organize that review. They are voluntary assessment resources, not platform certifications, proof that a particular deployment is safe, or confirmation that it complies with applicable law.
Start by defining the deployment, not the vendor’s safety claims
A platform’s controls matter only in relation to how it will be used. The same service may present different risks when employees use it to draft internal text, when it processes sensitive information, or when its output influences a decision about another person. Before reviewing vendor materials, make the proposed deployment concrete enough that you can tell which safeguards are relevant.
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- Task and purpose: What work will the platform perform, and what will it not be used to do?
- Users and affected people: Who can enter information or act on an output, and who may be affected without using the platform themselves?
- Data: What information will be submitted, received, stored, or otherwise processed? Identify sensitive or confidential information relevant to your use.
- Access and boundaries: Which integrations, systems, tools, or permissions will be available to the service?
- Decisions and consequences: What actions or decisions could depend on an output, and what is the impact if that output is wrong, incomplete, or misunderstood?
- Requirements and tolerance: What organizational requirements and applicable obligations matter, and what level of residual risk can your organization accept?
This definition is the reference point for the review. If the vendor’s evidence covers a different task, user group, configuration, or operating context, it may not answer the question you need to decide.
Choose a framework that fits the system
NIST AI RMF 1.0 is a voluntary framework for organizing AI risk work. NIST released it on January 26, 2023. For generative AI, the companion NIST AI 600-1 Generative AI Profile, released July 26, 2024, adapts the general framework to generative AI in light of a user’s goals, requirements, risk tolerance, and resources. Use the profile as a way to structure questions about a generative AI deployment, not as a product approval.
NIST describes the purpose of the framework this way: “The Framework is intended to help developers, users and evaluators of AI systems better manage AI risks which could affect individuals, organizations, society, or the environment.” That is a risk-management purpose, not an assurance that a particular system will meet every organization’s needs.
Review several trustworthiness properties, not one “safety” label
Safety is one part of a broader assessment. NIST’s AI RMF identifies trustworthiness characteristics that include validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness, with harmful bias managed. They are useful lenses for identifying different kinds of risk, but they are not interchangeable. For example, an explanation of an output does not by itself show that the output is reliable, and a security statement does not establish that a system is appropriate for a particular decision.
Use the characteristics to ask what could go wrong in your defined deployment and what evidence would help you judge whether the vendor’s controls address it. The relevant questions depend on the work, data, users, affected people, and operating conditions. NIST’s materials place these considerations across the AI system lifecycle, including pre-design, design and development, deployment, use, and test and evaluation.
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| Assessment area | Evidence to seek | Decision question |
|---|---|---|
| Intended-use fit | Documented tasks, boundaries, assumptions, and known limitations | Does the evidence cover our use and the people affected by it? |
| Safety, reliability, and performance | Evaluation methods, representative results, uncertainty measures, and failure handling | What happens in the cases that matter to our workflow? |
| Security and resilience | Product-specific security information, access boundaries, and resilience practices | Can the service and connected components withstand relevant misuse or disruption? |
| Privacy | Data-flow and privacy-risk information, relevant use and retention terms, and controls | Is the processing appropriate for our data and jurisdiction? |
| Accountability and transparency | Ownership, documentation, explanations, and escalation routes | Can staff understand, challenge, and report problematic outputs? |
| Lifecycle governance | Change notifications, review processes, incident handling, and monitoring | Who notices and responds when the model, service, or use changes? |
| Residual risk | Documented gaps, mitigations, owners, and an acceptance decision | Can our organization accept and manage what remains? |
This table is an assessment aid synthesized from NIST’s trustworthiness characteristics and AI RMF Core outcomes; it is not a scorecard published by NIST. It is intended to make the review consistent, not to produce a universal pass mark.
Request evidence tied to the exact product and configuration
Ask for current, product-specific documentation rather than relying on a general policy statement or marketing description. Record what each document covers: product or service, version, date, assumptions, exclusions, and the configuration or deployment conditions it addresses. If those details are missing, ask the vendor to clarify them before treating the evidence as relevant.
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Ask how the system’s intended tasks and boundaries are defined
Seek documentation of the tasks the system is designed to perform, the context in which it is intended to be used, and known assumptions or limitations. Compare those boundaries with your deployment definition. If your use depends on a capability or operating condition that the documentation does not address, record that as an evidence gap rather than assuming the platform will behave as needed.
Ask how performance and uncertainty are evaluated
Look for the methods used to test the system, what measures are reported, how uncertainty is assessed, whether benchmark comparisons are used, and how results are documented. Ask what the results do—and do not—cover. A performance figure or benchmark without the method, task, conditions, and scope is difficult to apply to your workflow. Results from a different setting should not be treated as evidence for yours without a reasoned basis.
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Ask how validation and explanations are documented
Seek documentation of model validation and explanation, including how outputs are interpreted in context. An explanation may help people examine an output, but it should not be treated as a substitute for evidence about performance or suitability. Decide what staff need to understand, challenge, or escalate when using outputs in the actual workflow.
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Request information about the data involved and how privacy risk is examined and documented. Check it against the information your use will process and the jurisdiction or organizational requirements relevant to that use. If a vendor’s materials do not cover your data flows or deployment conditions, do not assume that a general privacy statement resolves the gap.
Ask who owns governance and change review
Look for named governance roles and documented lifecycle processes, including how reviews and changes are handled. Clarify how your organization can learn about changes relevant to its use, who handles incidents or exceptions, and how a change in the service, configuration, or intended use will trigger review.
Distinguish among a policy statement, operational records, and independent assurance. A policy may describe an intended practice; records can show activities or results; independent assurance may provide another form of evidence. The assessment should note what each item actually establishes. The NIST materials support examining these outcomes, but do not establish that every supplier must provide every item in one particular format.
Evaluate the buyer’s deployment, not just the vendor’s materials
Vendor documentation cannot by itself establish whether a configuration is suitable for your workflow. Where feasible, evaluate representative tasks and foreseeable failure cases using the intended data, permissions, integrations, and human review arrangements. The aim is to test the deployment you are considering, not a generic demonstration or a configuration materially different from the one you plan to use.
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- Choose representative tasks. Select examples that reflect the work, inputs, users, and operating conditions you expect. Include ordinary cases as well as consequential or difficult cases relevant to your use.
- Define what acceptable performance means for the task. Decide what staff should check, what errors matter, and what should happen when an output is uncertain, incomplete, or unsuitable. Do not assume that the vendor’s general performance measures answer these local questions.
- Use the intended configuration. Test the permissions, integrations, and human review process that will actually be in place. Differences from the reviewed configuration can change the risks and the relevance of earlier findings.
- Record results and limitations. Keep the test conditions, observed failures, relevant evidence, and unresolved questions together. Note what the evaluation cannot establish as well as what it supports.
- Set escalation paths. Decide who can stop or question use, how problematic outputs are reported, and what happens when a case falls outside the documented boundaries.
NIST’s AI RMF Core calls for rigorous testing and performance assessment and documented validation. It does not provide a universal test suite, and a limited evaluation does not prove that a platform or deployment is safe. Treat your test as one part of an evidence-based decision, not as a guarantee about every future input or condition.
Compare candidates against the same use case
If you are considering more than one platform, use the same deployment definition, evidence requests, and decision questions for each candidate. Otherwise, an apparent difference may reflect uneven documentation or different assumptions rather than a meaningful difference in risk. Assess the evidence available for your particular use; do not infer that a supplier is safer simply because its claims are broader or its materials are more polished.
A useful comparison records evidence, gaps, and their implications—not an unexplained numerical score. For each candidate, note what the material covers, what remains unknown, and whether proposed mitigations are realistic for the people and processes that will operate the system.
- Intended-use fit and residual risk: Are the documented tasks and limits relevant, and can the remaining risks be managed?
- Testing and validation: Are methods and results relevant to the workflow, and are uncertainty and limitations documented?
- Privacy and data handling: Does the evidence address the information and processing involved in this deployment?
- Security and resilience: Are access boundaries and relevant resilience practices documented for the service and its connected components?
- Accountability and transparency: Can responsible staff understand, challenge, and report outputs or problems?
- Lifecycle governance: Are there review, change, incident, and monitoring processes that can support the planned use?
NIST’s framework and Core support these assessment dimensions, but they do not supply a standard vendor scorecard, comparable results for specific platforms, or a universal threshold for adoption. Your organization must decide what evidence is sufficient for its context and who has authority to accept the remaining risk.
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Make a documented decision about what remains
For each material risk or evidence gap, record the issue, its relevance to the intended use, any mitigation, who owns that mitigation, and who is authorized to accept what remains. A decision should distinguish evidence that supports use from assumptions, unresolved questions, and conditions that must be in place before use can proceed.
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Possible outcomes include proceeding within documented boundaries, proceeding only after specified controls or reviews are in place, delaying a decision while material evidence is missing, or declining the deployment when the remaining risk cannot be managed. Those are organizational choices, not NIST-defined certification outcomes. A framework-alignment claim can help organize a conversation, but it should not replace review of actual evidence or a separate assessment of applicable obligations.
Plan oversight for changes and incidents
Adoption is not the end of the assessment. NIST treats governance as continual, and its framework spans the AI system lifecycle. Assign responsibility for reviewing incidents, changes, performance, and exceptions before the platform is in routine use. Define who maintains the record of the decision and who can require a reassessment.
Set out what will trigger a review. Depending on the deployment, that may include a change in the use, configuration, model, or operating context; an incident or unexpected failure; or evidence that performance or assumptions no longer match the workflow. The review should ask whether the original evidence still applies, whether controls remain in place, and whether the risk decision needs to change.
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Keep the review process usable: identify the owner, the route for reporting problems, the information to record, and the person or group that can pause or change the use. Without ownership and a response path, a documented control can exist on paper while problems go unexamined in practice.
Keep framework alignment, safety, and legal compliance distinct
NIST AI RMF is voluntary, and the AI RMF Playbook provides suggested actions for outcomes in the Core. Neither is a certification of a platform, a finding that a specific deployment is safe, nor a legal opinion. Using the framework can make questions and responsibilities more systematic; it does not remove the need to judge evidence in context.
Legal obligations depend on the deployment and jurisdiction. Because the relevant industry, location, platform, and intended use vary, a general framework review cannot determine which requirements apply to a particular buyer. Check current authoritative legal sources for the actual deployment and obtain qualified advice where needed. Likewise, verify the product version and configuration under review: vendor documentation and implementations can change.
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