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Build an AI vendor checklist around the specific use case, data, and consequences of failure—not around a generic score or a vendor’s assurances. Extend your existing procurement, security, privacy, and third-party risk reviews with AI-specific questions, request evidence for the answers, and record who accepts any residual risk. Then reassess when the system, its dependencies, or its risk changes.
Use NIST as a structure, not a universal compliance checklist
NIST’s AI Risk Management Framework (AI RMF) organizes risk work across four functions: Govern, Map, Measure, and Manage. Its AI RMF 1.0 was released on January 26, 2023. NIST describes the framework as voluntary guidance intended to help incorporate trustworthiness considerations throughout the AI lifecycle; it does not prescribe one numerical score or approval threshold for every organization.
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NIST’s AI RMF Playbook offers suggested actions and explicitly is not a checklist or a sequence that every organization must follow in full. Use it to shape your process, then tailor questions and controls to the product, deployment, data, potential impact, and your organization’s risk tolerance. For generative AI-specific supplier considerations, NIST published the Generative AI Profile (AI 600-1) on July 26, 2024. NIST SP 1326, the Due Diligence Assessment Quick-Start Guide, is dated October 30, 2024.
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1. Define the use case and review scope
Before sending a questionnaire, write down what the organization wants to use the product for and where the vendor’s system sits in the workflow. A vendor may describe a broad capability, but your assessment should cover the particular deployment being considered.
- Product and ownership: vendor, product or service, business owner, model or service components, and version or release if known.
- Purpose and boundaries: intended tasks, prohibited or out-of-scope uses, intended users, and people who may be affected by outputs or decisions.
- Data: categories of information entering, leaving, or retained by the service, including personal information, confidential business material, and intellectual property.
- Deployment: hosted service, API, embedded feature, on-premises component, or a model supplied to another system; note integrations and access to organizational systems.
- Value chain: other vendors, subprocessors, pretrained models, datasets, tools, connectors, and services involved in providing or operating the system.
- Potential consequences: plausible safety, financial, operational, rights-related, reputational, and security harms if the system fails, behaves unexpectedly, or is misused.
Use these facts to assign a review tier under your organization’s existing risk method. For example, a use involving sensitive data or consequential decisions may warrant more evidence and review than a low-impact internal convenience feature. That is an organizational risk decision, not a tiering formula supplied by NIST.
2. Build the checklist around the AI RMF lifecycle
Govern: accountability and oversight
- Who at the vendor is accountable for AI risk, and who can explain or approve material changes?
- What governance processes oversee design, release, deployment, and ongoing monitoring?
- Where relevant, does the vendor keep an inventory of approved generative AI providers and third parties that can access organizational content?
- What documentation, audit, or evaluation rights can your organization exercise?
- Will the vendor notify you of material system changes, incidents, or relevant new dependencies?
Ask for the policies, inventories, and contract terms that support the answers. NIST’s Generative AI Profile recommends maintaining approved provider lists and inventories of third parties with access to organizational content, and recommends contractual terms that allow evaluation of third-party generative AI processes and standards.
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- What information does the service receive, generate, store, or transmit? Where does it flow, and which parties or personnel can access it?
- Is customer data used to train, fine-tune, or otherwise improve models? If yes, under what controls, customer choices, and contractual terms?
- What are the retention, deletion, backup, and post-termination handling practices?
- What privacy assessments and safeguards address personal information and the people affected by its use?
- What are the sources, permissions, and provenance controls for training, fine-tuning, retrieval, and evaluation data?
- How are rights in customer inputs, outputs, and third-party content allocated and protected?
- Can the vendor describe data lineage and content provenance, including relevant sources, timestamps, or metadata?
Request supporting terms and records rather than treating statements about training data, copyright, or data handling as independently verified. NIST’s Generative AI Profile calls for acquisition due diligence on intellectual property, privacy, and security, and recommends retaining records of third-party changes to content to support provenance.
Measure: security, supply chain, and system behavior
- Which access controls, authentication, encryption, logging, vulnerability management, secure development, and incident response controls apply to the service and its AI components?
- What access to data or systems do plugins, tools, agents, connectors, and subprocessors receive?
- Which third parties can access organizational content, and how are they assessed and monitored?
- What independent assurance reports or test summaries are available? Record their scope, date, and the systems they cover.
- How does the vendor disclose and manage material vulnerabilities, supply-chain changes, and security incidents?
- What tasks is the system intended to perform, and what limitations does the vendor document?
- What evaluations were conducted for your intended use, and which populations, languages, data, and operating conditions were represented?
- What testing addresses accuracy, robustness, safety, harmful bias, privacy, security, or foreseeable misuse where relevant to the use case?
- How are outputs reviewed, users informed, humans involved, and issues escalated?
- How could model or service changes alter behavior, and how are changes tested and communicated?
- Can your organization conduct an evaluation or obtain sufficient evidence without requiring disclosure of protected proprietary details?
There is no single standardized test suite that is appropriate for every AI system. Ask for evaluation evidence relevant to the task and conditions in which you intend to use the product. NIST’s Playbook connects AI RMF outcomes to measurement, testing, evaluation, verification, and validation activities. NIST SP 1326 describes supplier due diligence for cybersecurity supply-chain risk and the need for supplier-risk information before procurement decisions.
Manage: contracts, operations, and exit
- Do contract terms define permitted use, data handling, security commitments, incident notice, subprocessor controls, evaluation rights, and change notice?
- Are service continuity, fallback arrangements, and responsibilities during an incident or outage clear?
- Will the vendor cooperate with investigations and remediation, and how will relevant evidence be retained?
- At contract end, how will data be returned or deleted and access terminated?
- What contingency arrangements exist if the vendor, model provider, or another critical third party fails?
- What reassessment interval and event triggers are appropriate for this level of risk?
NIST’s Generative AI Profile recommends contractual clauses that permit evaluation of third-party generative AI processes and standards, along with contingency planning for failures or incidents involving high-risk third parties.
3. Request evidence and document gaps
For each checklist answer, request a document, record, or demonstration that is relevant to the product and deployment under review. An assurance is useful as a lead, not a substitute for evidence. Examples include data-handling terms, policies, evaluation summaries, incident procedures, subprocessor lists, and change-management information.
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4. Make approval a recorded risk decision
Use the organization’s existing risk-rating method where possible. NIST’s AI RMF supports lifecycle risk management, but the cited NIST materials do not define a universal score or mandatory approval threshold. A transparent workflow is:
- Set inherent risk: classify the use case using factors such as potential impact, data sensitivity, exposure, and deployment context.
- Collect answers and evidence: record vendor responses and flag missing, outdated, or out-of-scope support.
- Rate the domains: apply your organization’s method consistently and explain the rationale for each rating.
- Record treatment: document existing or compensating controls, remaining risk, remediation owner, and due date.
- Route the decision: send exceptions to an authorized risk owner and record approval conditions, or the reasons to reject or defer.
- Set follow-up: identify the monitoring owner and the events that require reassessment.
The decision record should make clear what was assessed, what evidence informed the decision, who owns the remaining risk, and what conditions must be met before or during use. Do not let a single vendor score obscure a serious issue in a consequential domain.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Compare vendors on the same basis
Send candidates the same use-case description and evidence requests, then compare what each has demonstrated—not just its questionnaire answers or feature claims. Use the following dimensions as a common review frame:
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|---|---|
| Data use and privacy | Data flows, training or improvement use, retention and deletion terms, and privacy safeguards. |
| Security evidence | Controls and assurance evidence, including its scope, date, and coverage of relevant components. |
| System evaluation | Evidence relevant to the intended task, users, languages, and operating conditions. |
| Transparency and change | Visibility into limitations, material changes, incidents, and new dependencies. |
| Supply-chain visibility | Third-party access, dependency information, and monitoring practices. |
| Continuity and response | Incident handling, fallback options, and contingency arrangements for critical failures. |
| Contractual rights | Ability to evaluate processes and standards, receive relevant notices, and obtain cooperation. |
| Deployment fit and residual risk | Compatibility with your integration boundaries and remaining risk against your organization’s tolerance. |
The cited NIST guidance supports evaluating these areas but does not rank vendors or prescribe universal weights. Apply weights only if your organization can explain why they reflect the use case and risk tolerance.
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6. Monitor changes and keep an exit path
Approval covers a particular system, configuration, evidence set, and intended use. Maintain an owner and a monitoring plan proportionate to the risk. Reopen the assessment when a material change could alter data handling, system behavior, access, or the consequences of failure.
- Track material model or service releases and changes in subprocessors, tools, or other dependencies.
- Review relevant incidents, vulnerabilities, performance issues, and new evidence from the vendor.
- Confirm that contract conditions, data-use limits, and required mitigations remain in place.
- Use a contingency or fallback process for high-risk deployments if the vendor or a critical third party becomes unavailable or unsafe to use.
- At offboarding, verify data return or deletion and revoke integrations, credentials, and other access.
Reassessment triggers and intervals should reflect the deployment’s risk; the NIST materials do not establish one schedule for every organization.
Scope and legal considerations
NIST guidance is a framework for risk management, not legal advice or a substitute for identifying obligations that apply to your organization. Because legal duties depend on factors such as jurisdiction, industry, data type, and deployment context, have the appropriate legal, privacy, security, and business owners determine which requirements apply to the specific use.
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