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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsEvaluate an enterprise AI vendor by matching documented evidence and contract terms to the system’s actual use—not by accepting a general claim that it is “safe.” Define the use and system boundary, assess governance and impacts, examine test evidence, set operational safeguards, and monitor the service after deployment. NIST’s AI Risk Management Framework (AI RMF) is a useful structure for that work, but NIST explicitly says its actions “do not constitute a checklist, nor are they necessarily an ordered set of steps.”
How to use this checklist
Use the questions below to structure diligence, not as a universal pass/fail form. The right depth depends on what the AI does, who may be affected, what data it handles, how it is deployed, and your organization’s risk tolerance. Apply the same evidence standard to every candidate vendor.
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- Define the proposed use. Record the task, users, affected people, operating context, expected benefits, foreseeable misuse, and consequences of an error.
- Map the complete service. Include the vendor’s model and any fine-tunes, APIs, retrieval or grounding sources, plugins, libraries, embedded AI, and subcontractors involved in processing or delivering the service.
- Request evidence for that use. Ask for documentation of relevant tests, measures, limitations, and reviews—not an unqualified assurance that the product is responsible or safe.
- Set conditions before approval. Assign internal owners, define human oversight and incident procedures, and negotiate continuing vendor obligations.
- Review after deployment. Track service and model changes, incidents, user feedback, and whether the system remains within its approved use and risk tolerance.
NIST released AI RMF 1.0 on January 26, 2023, and says the framework is being revised. Its Generative AI Profile, NIST AI 600-1, was released July 26, 2024. Both are voluntary guidance; neither is a universal certification or a legal safe harbor.
Govern: establish responsibility and accountability
Governance should continue throughout the system’s life, from acquisition through operation and eventual decommissioning. Ask the vendor and your internal teams to make responsibility concrete.
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- Who owns safety, privacy, security, incident response, and change control on each side?
- What policies govern acceptable use, human oversight, escalation, and decommissioning?
- How does the vendor inventory the AI components in the service and review risk over time?
- What independent assessments, evaluations, or audits have been completed, and exactly which product version, components, and use cases were in scope?
- What limitations apply to any assurance, certification, or audit report the vendor offers?
- Who in your organization can approve the residual risk, and what level of risk is acceptable for this use?
Ask for evidence that explains the scope and limits of an assurance. A certificate or audit label alone does not establish that the specific workflow, configuration, data, or deployment context has been assessed.
Map: define the system boundary, use, and impact
Describe what the service actually does in your environment. A vendor’s product name or broad description may not reveal which model, data sources, integrations, or third parties are involved in a particular deployment.
- What task does the AI perform, who uses its output, and where does it sit in the workflow?
- What information enters the service, where is it processed, and which vendor personnel or third-party components can access it?
- Which base models, fine-tunes, tools, APIs, plugins, retrieval sources, libraries, and embedded technologies are part of the service?
- What are the documented knowledge limits, assumptions, known failure modes, and prohibited or unsupported uses?
- Who could be affected by an incorrect or inappropriate output—for example, customers, employees, or applicants—and could the effects differ across groups?
- Which laws, regulations, contracts, and internal policies apply to this use and to each party’s role?
NIST’s Generative AI Profile recommends updating acquisition and procurement diligence to address risks such as intellectual property, privacy, security, embedded technologies, third-party monitoring, libraries, APIs, fine-tuned models, and incident or vulnerability information.
Measure: request evidence, not broad assurances
Ask for documentation tied to the system version and use you are considering. Performance in a vendor’s test environment does not by itself show how the service will behave with your data, integrations, users, and operating conditions.
- Evaluation scope: Which version and components were tested? What use cases, test datasets, and conditions were included, and what are the datasets’ limitations?
- Results and limits: What metrics, acceptance thresholds, uncertainty, and performance limits were reported? How representative were the tests of the planned deployment?
- Relevant failure modes: Depending on the use, was the system assessed for foreseeable misuse, prompt or input attacks, data exposure, harmful or biased outputs, and security failures?
- Review: Was testing internal, independent, or both? What human review took place, and are there unresolved findings or disagreements?
- Ongoing evaluation: How are production behavior, model changes, incidents, user feedback, and emerging risks tracked?
- Gaps: Which dimensions were not tested or cannot currently be measured?
NIST recommends testing AI systems before deployment and regularly during operation. Its AI RMF calls for documenting tests, metrics, tools, performance limits, and relevant evaluations of safety, security, privacy, fairness, transparency, and accountability. Ask the vendor to distinguish measured results from areas where evidence is incomplete.
Assess data protection and the supply chain
Treat the data path and the service’s dependencies as part of the system under review. Establish who can access organizational content and what happens to it throughout processing and afterward.
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- Is input or output data retained? If so, for how long, where, and for what purpose?
- Can organizational data be reused for model training, fine-tuning, product improvement, or another customer’s service?
- Which subprocessors or third parties receive or can access data, and what functions do they perform?
- What privacy and security controls apply to data in transit, at rest, and in vendor support or troubleshooting workflows?
- How does the vendor handle intellectual-property concerns, data deletion, access requests, and security incidents?
- How will you be notified if a model, data source, subprocessor, or other material dependency changes?
Request the vendor’s relevant data-flow and subprocessor information, and compare it with your own data classification and contractual requirements. If the vendor cannot identify the components or parties involved, treat that as an unresolved visibility issue rather than assuming the service boundary ends at the vendor’s interface.
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Safeguards should specify who acts, what triggers action, and how the service can be limited if it behaves unexpectedly. Match the level of human review to the potential impact of the output.
- Which outputs require human review before they affect a person or trigger a consequential action?
- How can users report a problem, seek correction, or obtain recourse where relevant?
- Who receives incident reports, who leads response, and how and when will affected customers be notified?
- Can the service be paused or restricted, and is there a manual process or alternative service if it is unavailable or unsuitable?
- What model, data-source, or subprocessor changes trigger reassessment, restriction, rollback, suspension, or termination?
- What support availability and response times are needed for your use, including during an outage or serious incident?
Document internal owners and escalation paths alongside the vendor’s commitments. NIST recommends third-party incident-response planning, ongoing monitoring, contingency and fallback planning, and contract terms addressing incidents, liability, system changes, notifications, support availability, and response times.
Compare vendors on consistent evidence
Use the same questions and evidence standard for each candidate. The axes below are a practical synthesis of NIST’s risk-based approach, not an official NIST scorecard or ranking method.
| Comparison axis | Evidence to compare |
|---|---|
| Use fit and limits | Documented intended uses, known limitations, deployment fit, and boundaries on use. |
| Test quality | Evaluation scope, test representativeness, metrics, uncertainty, independent review, and deployment-like testing. |
| Data protection | Data access, retention, reuse, privacy assessment, and security controls. |
| Supply-chain visibility | Models, APIs, subcontractors, plugins, third-party data, and change notification. |
| Human oversight | Review points, escalation, user feedback, and appeal or recourse where relevant. |
| Operational resilience | Incident response, fallback, support, recovery, and safe shutdown. |
| Accountability | Contractual responsibility, evaluation or audit rights, notifications, and service commitments. |
| Risk fit | Residual risks measured against your documented risk tolerance and the impact of the proposed use. |
Record evidence gaps as well as strengths. A vendor that provides fewer claims but clearer test scope and limitations may be easier to assess than one offering broad assurances without supporting detail.
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Set contract conditions and ongoing review
Procurement approval should not be a one-time decision for a service that can change after launch. Have legal, security, privacy, procurement, and business owners agree on obligations that make the approved use maintainable.
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- Rights to evaluate relevant vendor processes and obtain information needed for your risk reviews.
- Notice of material changes to models, data sources, subprocessors, or service behavior.
- Disclosure and cooperation commitments for serious incidents, including response and support expectations.
- Clear allocation of responsibilities and workable terms for liability, suspension, termination, and transition or fallback.
- Defined service commitments, escalation contacts, and response times appropriate to the business impact.
- Internal triggers for periodic review and for immediate reassessment after an incident, significant change, or evidence of unexpected behavior.
Assign an owner to monitor the deployed service and keep a record of the approved purpose, configuration, residual risks, vendor evidence, and conditions for re-review. If actual use expands beyond the evaluated context, treat it as a new risk decision rather than assuming the original approval covers it.
Apply legal guidance to the specific use and jurisdiction
Do not label every AI service “high-risk,” and do not assume a vendor’s compliance statement resolves your organization’s obligations. Determine the system’s use, the roles of provider, deployer, and other parties, and the law applicable to the relevant jurisdiction. The European Commission page reviewed describes draft guidance on high-risk classification and says it is not legally binding; it should not be treated as a final legal determination. Check current official materials and obtain jurisdiction-specific legal advice when needed.
NIST SP 800-63-4 addresses digital identity. In that context, it says organizations using AI/ML or relying on such services should implement the AI RMF and must document privacy risk assessments for personal information processed by those systems. It also calls for specified information about training methods, datasets, model update frequency, and testing results. These statements belong to that guidance’s scope; they are not universal requirements for every enterprise AI purchase.
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