The Tool Desk
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Start by defining the system and the decision it will support
Before comparing vendors, write down the proposed use in operational terms. “Use AI to improve customer service” is too broad to assess; specify what the system will do, who will use it, what decisions or actions may follow, and who could be affected. The same product can present very different risks in different settings.
- Task and users: What inputs will the system receive, what outputs or actions will it produce, and which employees, customers, applicants, or other people will interact with or be affected by it?
- Deployment context: Where will it run, what existing systems will connect to it, and what will happen when its output is wrong, incomplete, or unavailable?
- Benefits and harms: What intended benefit justifies using it? What foreseeable errors or misuse could cause harm, and could those effects differ across groups or uses?
- Limits and boundaries: What uses does the vendor intend or prohibit? What assumptions, known failure modes, and knowledge limits are documented?
- Risk tolerance: What level of residual risk can your organization accept for this particular use, given its impact and available alternatives?
- Applicable rules: Which laws, regulations, contractual commitments, and internal policies govern this use and the roles your organization and the vendor perform?
Record the answers as the scope for diligence and later monitoring. NIST AI RMF 1.0, released January 26, 2023, organizes risk work into four functions—Govern, Map, Measure, and Manage—and NIST says the framework is being revised. It is voluntary guidance, not a universal certification or legal safe harbor. NIST’s Generative AI Profile, AI 600-1, was released July 26, 2024.
Map the full service, data, and supply chain
Assess the service you will actually deploy, not just the vendor’s branded product or headline model. Ask the vendor to identify the components and parties that contribute to the system and explain how they may change.
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- Which base models, fine-tunes, libraries, APIs, plugins, retrieval or grounding sources, and embedded AI technologies are involved?
- Which subcontractors and other third parties process inputs, outputs, logs, or connected data? Which parties can access your organization’s content?
- What information will enter the service, where will it be processed, and how long will it be retained?
- Will data be reused, shared, or exposed to model training or improvement processes? Ask for the applicable settings and contractual commitments, not only a general privacy statement.
- How are privacy, information security, intellectual-property concerns, and third-party dependencies assessed and managed?
- What advance notice will you receive if a model, data source, subprocessor, or other material component changes?
NIST’s Generative AI Profile recommends updating procurement diligence to address risks including intellectual property, data privacy and security, embedded technologies, third-party monitoring, libraries, APIs, fine-tuned models, and incident or vulnerability information.
Request evidence that matches the proposed use
Ask for supporting documentation rather than relying on labels such as “responsible,” “secure,” or “safe.” Evidence is useful only when its scope, version, conditions, and limits are clear enough to compare with your intended deployment.
- Test scope: Which product and model version was tested, when, by whom, and for which intended uses? Request the evaluation scope and test datasets, including their limitations and representativeness.
- Results and uncertainty: What metrics, acceptance thresholds, uncertainty, and performance limits were reported? Ask for results under conditions similar to your planned deployment.
- Relevant failure testing: Depending on the use, what testing covers foreseeable misuse, prompt or input attacks, data exposure, harmful or biased outputs, and security failures?
- Review and unresolved issues: Was there independent or internal review, what was included, and were there disagreements, unresolved findings, or exclusions?
- Coverage gaps: Which risk dimensions were not tested or cannot currently be measured?
- Production feedback: How does the vendor track real-world behavior, incidents, user feedback, model changes, and newly identified risks?
Evaluate reliability, safety, security, privacy, fairness, transparency, and accountability as they apply to your context. NIST’s AI RMF Core calls for documenting evaluations, tests, metrics, tools, performance limits, and relevant risk dimensions. It also recommends testing before deployment and regularly during operation; a one-time evaluation does not establish that later versions or conditions are equivalent.
Turn oversight and incident response into operating procedures
Decide before launch who can rely on outputs, when a person must review them, and what happens when the service behaves unexpectedly. The vendor’s controls do not replace the buyer’s responsibility to decide how the system is used in its own environment.
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- Name buyer-side and vendor-side owners for safety, privacy, security, incident response, and change control.
- Set human review points, escalation routes, and end-user reporting channels; provide appeal or recourse where the use calls for it.
- Define how users can report harmful or incorrect behavior and how reports will be triaged, investigated, and resolved.
- Agree on an incident path: who is contacted, who leads the response, what information is shared, and how remediation is tracked.
- Specify when the service can be paused or restricted, and what manual process, fallback, or alternative service will keep essential work operating.
- Set review triggers for material changes, new failure modes, incidents, or evidence that the system no longer fits its approved use.
NIST’s AI RMF guidance calls for ongoing monitoring, contingency and fallback planning, and third-party incident response planning. It also identifies incident, liability, system-change, notification, support-availability, and response-time terms as matters to address in contracts.
Make vendor assurances enforceable and reviewable
Translate the controls you rely on into specific commitments in the agreement and related service documents. A statement in a sales presentation is not a substitute for a defined obligation, a way to verify it, or a remedy if it is not met.
- Seek rights to evaluate relevant vendor processes and evidence, with a clear scope and workable method.
- Define what counts as a material change and require notice early enough for your organization to assess it before or promptly after deployment, as appropriate.
- Set serious-incident disclosure, cooperation, support, and response commitments, including practical timelines.
- Allocate responsibility for the vendor’s service and your organization’s deployment decisions, including incident handling and remediation.
- Agree on usable termination, suspension, data return or deletion, and fallback terms if risk becomes unacceptable or service continuity fails.
- Maintain a named owner and review cadence so approval is revisited as the service, use, or risk changes.
These terms make procurement a continuing governance activity rather than a one-time approval. NIST’s AI RMF treats governance as spanning the system lifecycle, including clear roles, risk tolerance, monitoring, review, and safe decommissioning.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use the same evidence standard to compare candidates
For multiple vendors, ask the same questions and assess evidence against the same intended use. This comparison grid is a practical synthesis of NIST’s risk-based approach, not an official NIST scoring rubric or ranking method.
Best Value
| Comparison axis | Evidence to compare |
|---|---|
| Use fit and limits | Documented intended use, known limitations, fit with your deployment context, and boundaries on use. |
| Test quality | Evaluation scope, dataset relevance, metrics, uncertainty, independent review, and testing under deployment-like conditions. |
| Data protection | Data access, processing, retention, reuse, privacy assessment, and security controls. |
| Supply-chain visibility | Models, APIs, subcontractors, plugins, third-party data, and notice of material changes. |
| 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 rights, notifications, and service commitments. |
| Risk fit | Residual risks compared with your documented risk tolerance and the potential impact of the use. |
Scope legal conclusions to the jurisdiction and use
Do not assume that every AI service is legally “high-risk,” or that a vendor’s compliance statement determines your organization’s obligations. Identify the system’s purpose and the roles of provider, deployer, and other parties, then assess the current law that applies to that use and jurisdiction.
The European Commission page reviewed describes draft guidance on high-risk classification and says the guidance is not legally binding, although it reflects the Commission’s interpretation. It is not a final legal determination. Regulatory materials can change, so confirm the current position with appropriate legal counsel before relying on a classification.
NIST Special Publication 800-63-4 includes AI/ML statements specifically in the context of digital identity. It says organizations using AI/ML or relying on such services should implement the AI RMF, and calls for documented 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. Treat these as statements within that guidance’s scope, not universal requirements for every enterprise AI purchase.
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