Before adopting an enterprise AI service, require evidence that it fits the specific work you plan to use it for, understand how it handles your data, and put responsibilities for testing, changes, incidents, and exit into the contract. Then test the service on representative cases and decide whether the risks you cannot resolve are acceptable for that use.
How should you define the use case before assessing a provider?
Start with the business process, not the provider’s product description. Record what the AI service will do, who will use or rely on it, who may be affected by its output, which data and systems it will touch, and what a useful result looks like. Specify what could go wrong if an answer is inaccurate, delayed, unavailable, or exposed to the wrong person.
That definition sets the level of evidence you need. An assistant that drafts internal summaries has a different impact profile from a service that influences identity verification or another consequential decision. Identify acceptable error and failure conditions, and decide which outputs need human review or must not be used for a particular decision.
Use the scope to create a consistent basis for evaluating providers. Compare the same dimensions for each candidate, weighting them according to the consequences of the use case rather than treating a single certification or framework claim as a complete answer.
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| Evaluation dimension | What to establish |
|---|---|
| Use-case fit | Quality on the intended task, known limits, and performance on representative and failure scenarios. |
| Data, privacy, and security | What information enters or leaves the service, how it is protected and retained, and whether it is reused. |
| Transparency and evaluation | What system information, test evidence, records, and provider-process access are available to you. |
| Rights and provenance | Rights to inputs and outputs, content origins where available, and handling of third-party rights claims. |
| Resilience and support | Dependencies, incident handling, availability, support, and an operational alternative if the service fails. |
| Contractual accountability | Whether the provider’s commitments cover your requirements, including changes, incidents, and exit. |
What evidence should you ask an AI provider for?
Ask for information that is relevant to your use case and sufficiently specific to verify. A general assurance that a service is “secure,” “accurate,” or “responsible” does not establish how it will behave in your environment.
- System description: relevant models or components, functions, assumptions, limitations, and instructions for intended use.
- Data and training information: what the provider can disclose about training data and methods, and about information used at inference time.
- Testing: test methods and results relevant to your task, including the conditions and limitations of those results.
- Change practices: how often the service or model changes, how material changes are communicated, and what records are available.
- Evaluation access: whether you can assess the provider’s processes and standards, and what documentation or records it will make available.
Do not assume a provider will disclose proprietary technical details. Instead, record what it does disclose, what remains unavailable, and how each gap affects the decision. Run your own evaluation using representative inputs and failure cases; vendor documentation is not a substitute for testing the service in the context where your organization will use it.
For digital-identity services, NIST Special Publication 800-63-4 specifically discusses documenting and communicating AI/ML use and providing information about training methods, datasets, model update frequency, and testing results. That guidance is scoped to digital identity; it should not be presented as a universal requirement for every AI procurement.
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How will the provider use your data?
Map the complete data flow, from enterprise systems into the service and back. Include prompts and files, outputs, logs, integrations, and information accessible to the provider’s subprocessors. For each category, establish who can access it, where it is handled when location matters, how long it is retained, and how it is returned or deleted when the service ends.
Clarify whether the provider may use customer data, prompts, or outputs to train or improve services, or for any other secondary purpose. Do not rely on an assumption that enterprise use means no reuse; get the permitted uses in writing. Check whether settings or product tiers change those terms, and whether the same protections cover connected services and subprocessors.
Review the rights attached to inputs, outputs, and transformed content. Establish what rights each party retains, what rights the provider needs to deliver the service, what content-provenance information is available, and how the parties will handle a third-party rights claim. NIST’s Generative AI Profile calls for procurement due diligence that includes intellectual property, data privacy, security, and other risks, and recommends contracts addressing ownership, usage rights, security, quality, and provenance.
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How should you assess the supplier and its supply chain?
Evaluate the provider as a supplier, not only the model as a product. Identify material dependencies such as embedded models, APIs, data providers, and subcontractors. Ask how the provider manages vulnerabilities, unauthorized changes, and incidents relevant to the service, and what evidence supports its security claims.
NIST’s 2026 Special Publication 1326 organizes ICT-supplier due diligence around foreign ownership, control, or influence; provenance; resilience; foundational cybersecurity practices; and supply-chain tiers. These dimensions can structure questions where relevant, but SP 1326 is scoped to ICT suppliers. NIST’s Generative AI Profile also recommends maintaining an inventory of third parties that can access organizational content and an approved-provider list.
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What should the AI contract cover?
Turn material diligence findings into enforceable terms rather than leaving them in sales materials or informal assurances. Have counsel review the agreement for your jurisdiction, use case, and data. Address at least the following:
- Permitted data and content uses, including any secondary use, and ownership or licensing of inputs and outputs.
- Security, privacy, quality, provenance, and documentation commitments that match the deployment.
- Your rights to evaluate the provider or review relevant records, with clear scope and workable procedures.
- Advance notice and a response process for material service, model, or dependency changes.
- Incident responsibilities, notification timing, cooperation, and response expectations.
- Availability, support, and service expectations for the business process that depends on the AI.
- Liability allocation, termination rights, and terms that could create unexpected exposure or allow unauthorized data use.
- At termination, access to and export of data and records, deletion or return, and support for transition.
For a critical service, make the exit plan operational: identify who owns transition tasks, how long access continues, where exported records will go, and what alternative supplier or manual process can take over. A termination clause without a workable transition path may not protect the business process.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What happens if the AI service fails or changes?
Assign an internal owner for the service and define who can pause or restrict it. Agree on review triggers such as a material model or service change, a new subprocessor, a change in data or usage, a serious incident, or evidence that the risk profile has shifted.
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Before launch, rehearse a realistic failure or incident scenario. Establish how staff report a problem, who investigates and communicates with affected parties, how the service is suspended if needed, and how work continues while it is unavailable. For a vital dependency, test the fallback rather than merely documenting it. NIST’s Generative AI Profile and AI Risk Management Framework Playbook recommend ongoing monitoring, documented and rehearsed incident processes, and contingency or fallback arrangements for important third-party AI functions.
Keep the provider and material dependencies in an inventory, and revisit the decision when the use case or service changes. Adoption is not a one-time approval: the evidence that supported the original decision may no longer describe the service or its operating context.
How should you use NIST guidance in the decision?
NIST’s AI Risk Management Framework (AI RMF 1.0), released in 2023, is a voluntary framework for integrating trustworthiness considerations across AI design, development, use, and evaluation. Its Playbook offers practical guidance on understanding third-party functions, assumptions and limitations, and establishing testing and usage-instruction expectations. NIST’s Generative AI Profile, released July 26, 2024, applies risk-management practices to generative AI.
NIST’s AI RMF FAQ, updated August 13, 2026, describes the framework as a living document. NIST’s framework page says AI RMF 1.0 is being revised and records a critical-infrastructure profile concept note released April 7, 2026. Use the framework as a structured aid that you tailor to the particular service and risk. Alignment with a voluntary framework is not a certification, proof of provider performance, or substitute for your own testing and applicable legal review.
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When is the evidence sufficient to proceed?
Proceed only when the evidence and contract are proportionate to the use case and the remaining uncertainty fits your organization’s risk tolerance. A decision record should capture the intended use, evaluation results, unresolved evidence gaps, the people accepting residual risk, and the conditions that would trigger a pause or reassessment. If the provider cannot answer a material question, treat that as an explicit decision factor—not as proof that the associated risk is absent.
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