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Build an AI vendor risk assessment checklist around the specific use case, data, and consequences of failure—not around a generic list of AI features. Extend your existing procurement, security, privacy, and third-party risk reviews, then use the NIST AI Risk Management Framework (AI RMF) to organize the work from governance and scoping through measurement and ongoing management.
NIST guidance is voluntary, not a universal compliance checklist. Its AI RMF Playbook offers suggested actions, not a mandatory sequence to follow in full. Your organization must decide which questions and evidence are proportionate to the system and its risks.
Start by defining the use case and system boundary
Before asking a vendor to complete a questionnaire, write down what the organization intends to do with the AI system and where the system begins and ends. The same product can present different risks when used for different tasks, with different data, or by different people.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Purpose: What business task will the system support? Record permitted uses and uses that are out of scope.
- System boundary: Identify the product, model or service components, known version or release, integrations, and where human review occurs. Note whether the service is hosted, accessed through an API, embedded in another product, deployed on premises, or supplied as a model to another system.
- People: Identify intended users and people affected by the system’s outputs or decisions, including relevant populations, languages, and accessibility needs.
- Data: List what will enter, leave, or be retained by the service. Include personal information, confidential business information, intellectual property, prompts, uploaded files, retrieval sources, and generated outputs where applicable.
- Consequences: Consider plausible effects of errors, misuse, outages, or unauthorized disclosure on safety, rights, finances, operations, security, and reputation.
- Dependencies: Map known model providers, subprocessors, datasets, plugins, tools, connectors, and other services in the delivery chain.
Use this intake to determine how intensive the review should be. A system that handles sensitive data or can materially affect people may warrant deeper evidence review and tighter approval conditions than a low-impact internal assistant. NIST does not prescribe a mandatory tiering formula.
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Organize the review around the AI lifecycle
NIST AI RMF 1.0 was released on January 26, 2023, and NIST says it is being revised. Its four functions—Govern, Map, Measure, and Manage—provide a useful structure for organizing vendor questions and decisions. NIST describes the framework as intended for voluntary use to improve how trustworthiness is incorporated into AI design, development, use, and evaluation.
- Govern: Set accountability, review authority, policies, and contract expectations.
- Map: Document context, intended use, affected people, data, system boundaries, and potential impacts.
- Measure: Examine evaluation evidence and how the vendor tests relevant risks and system behavior.
- Manage: Decide how to mitigate, approve, monitor, reassess, or discontinue use as risks change.
The Generative AI Profile (NIST AI 600-1), published July 26, 2024, adds supplier-relevant considerations for generative AI, including due diligence on intellectual property, data privacy, and security; evaluation of third-party processes; and contingency planning for high-risk third-party failures. Apply those questions where they fit the product rather than assuming every vendor supplies a generative AI system.
Checklist: questions to ask and evidence to request
Ask vendors to answer each applicable question and identify the evidence that supports the answer. Record when a document was created, what product or service scope it covers, and whether it is current. Treat an assurance without supporting evidence as an assurance—not as independent verification.
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- Who at the vendor owns AI risk, and who is authorized to explain, approve, and communicate material system changes?
- What governance processes cover design, release, deployment, monitoring, and retirement?
- Does the vendor maintain an inventory of approved generative AI providers and third parties that can access organizational content, where relevant?
- What audit, assessment, or documentation rights can the customer exercise, including rights to evaluate third-party AI processes and standards?
- Will the vendor notify the organization about material changes, relevant new dependencies, and incidents?
Request: Governance policies or process summaries, a relevant third-party inventory, change-notice terms, and contract language describing customer evaluation or audit rights.
Data, privacy, and intellectual property
- What data does the service receive, generate, store, or transmit? Where does it flow, and which vendor staff or third parties can access it?
- Can customer data be used to train, fine-tune, or improve models? If so, what controls, customer choices, and contract terms apply?
- What are the retention, deletion, backup, and post-termination handling practices?
- What privacy assessments and safeguards address personal information and the people represented in or affected by that information?
- What are the sources, permissions, and provenance controls for training, fine-tuning, retrieval, and evaluation data?
- How are rights in customer inputs, generated outputs, and third-party content allocated and protected?
- Can the vendor describe data lineage and content provenance, such as sources, timestamps, or metadata where appropriate?
Request: Data-flow and retention descriptions, relevant privacy documentation, contract terms on data use and deletion, and available provenance or rights information. Do not treat a vendor’s statements about training data or copyright as independently verified unless supporting evidence is provided.
Security and supply chain
- What access controls, authentication, encryption, logging, vulnerability management, secure development, and incident response controls apply to the service and its AI components?
- What organizational data or system access is available to plugins, tools, agents, connectors, subprocessors, and model providers?
- Which third parties can access organizational content, and how does the vendor assess and monitor them?
- How are material vulnerabilities, supply-chain changes, and security incidents disclosed and managed?
- What contingency arrangements exist if the vendor, model provider, or another critical supplier fails?
Request: Independent assurance reports or test summaries with their scope and date clear, relevant security documentation, incident procedures, dependency information, and continuity plans. NIST SP 1326, the Due Diligence Assessment Quick-Start Guide dated October 30, 2024, emphasizes obtaining supplier-risk information before procurement decisions.
System behavior, quality, and evaluation
- What tasks is the system intended to perform, and what limitations or unsuitable uses does the vendor document?
- What evaluations were conducted for the intended task? Ask which populations, languages, data, and operating conditions were represented.
- What testing addresses accuracy, robustness, safety, harmful bias, privacy, security, and foreseeable misuse, as relevant to this use case?
- How does the vendor handle output review, user disclosure, human oversight, and escalation when an output may be wrong or harmful?
- Which model or service changes could alter behavior, and how are such changes evaluated and communicated?
- Can your organization conduct its own evaluation without requiring disclosure of protected proprietary details?
Request: Use-case-relevant evaluation summaries, test scope and limitations, documentation of known failure modes, and change-evaluation procedures. There is no single standardized test suite that applies to every AI system; the organization should judge evidence against the intended task and impact.
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Contracts, operations, and exit
- Do contract terms define permitted use, data handling, security commitments, incident notice, subprocessor controls, change notice, and customer evaluation rights?
- Who is responsible during a material incident, service outage, or model change, and what continuity or fallback arrangements apply?
- Will the vendor cooperate with investigations and remediation, and how will relevant evidence be preserved?
- At contract end, how will the organization retrieve its data, ensure deletion where agreed, and terminate access?
- What review intervals and event triggers will prompt reassessment?
Request: Contract terms, service-continuity procedures, incident cooperation commitments, data-return and deletion terms, and a documented change-notice process. For higher-risk uses, specify contingency responsibilities for failures or incidents involving the vendor or critical dependencies.
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Turn answers into a documented risk decision
Use the organization’s existing risk method where possible. NIST AI RMF supports lifecycle risk management, but neither it nor the Playbook defines a universal numerical score or mandatory approval threshold.
- Set an inherent-risk tier. Base it on the use case, data sensitivity, exposure, and potential impact. Document why the review depth is proportionate.
- Collect answers and evidence. Track missing, stale, incomplete, or out-of-scope evidence explicitly. Do not convert an unanswered question into an assumed pass.
- Rate the review domains. Apply the organization’s established ratings and record the rationale and evidence for each finding.
- Identify treatments. Record compensating controls, residual risk, remediation owner, and due date. If a risk cannot be reduced sufficiently, consider restricting or declining the use.
- Route the decision. Send exceptions to an authorized risk owner. Record approval conditions, reasons to reject or defer, and any limits on use.
- Set follow-up. Assign monitoring responsibility and reassessment triggers, including material system or subprocessor changes and incidents.
A useful approval record captures the product and use case reviewed, evidence dates and scope, findings, residual risk, accountable owner, mitigations, conditions, and next review trigger. This makes the decision explainable when the use changes or a concern arises.
Compare vendors on the same use case and evidence request
When evaluating candidates, give each vendor the same description of the intended task and ask for comparable evidence. Use the comparison to identify differences and unanswered questions—not to imply that a single universal weighting makes one vendor safer for every organization.
| Comparison area | What to compare |
|---|---|
| Data use and privacy | Data flows, training or improvement use, retention and deletion, privacy safeguards, and customer choices. |
| Security evidence | Controls relevant to the service, independent evidence and its scope/date, incident handling, and dependency oversight. |
| System evaluation | Testing relevant to the intended task, represented conditions and populations, known limitations, and opportunities for customer-led evaluation. |
| Transparency and change | Documentation of intended behavior, material-change notice, and evaluation of changes that may affect outputs. |
| Supply chain and continuity | Visibility into third parties, access to organizational content, monitoring, incident cooperation, and fallback arrangements. |
| Contract and residual risk | Evaluation rights, data and security commitments, exit terms, and remaining risk against your organization’s tolerance. |
Reassess after onboarding
Vendor approval is not a permanent finding about an AI system. Continue monitoring the matters that could change the risk of the specific use, and maintain a contingency process where a failure could have significant consequences.
- Reassess after a material model, product, integration, or subprocessor change.
- Review relevant incidents, security disclosures, and changes to data handling or retention.
- Check whether the actual use, user group, affected people, data, or deployment boundary has expanded beyond the approved scope.
- Confirm remediation items and approval conditions have been completed or renewed by the assigned owner.
- Keep an operational fallback or safe shutdown approach appropriate to the impact of service failure.
The legal and regulatory obligations for a particular deployment depend on its jurisdiction, sector, data, and use. This checklist is a risk-management aid, not legal advice; have the appropriate legal, privacy, security, and business owners determine applicable requirements.
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