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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchBefore choosing an AI vendor, define the intended use, the people affected, the data involved, the jurisdictions, and your organization’s role. Then assess the vendor, its supply chain, the system’s fitness and risks, the evidence available, and the contract terms for that specific use. No certificate, questionnaire, or framework alone establishes that an AI system is compliant or suitable for your business.
Start with the use case and your regulatory scope
A vendor review cannot produce a meaningful decision until the organization knows what it plans to use the AI for. The same product may present different risks when used to draft internal summaries, make recommendations about customers, or automate decisions affecting people.
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Write down the intended use
Describe the task, users, affected people, outputs, and what people will do with those outputs. Identify foreseeable misuse, including ways users could apply the system beyond its intended purpose. Note whether the system is a standalone model, an API, a fine-tuned model, or an AI feature embedded in another product; the relevant components and third parties may differ.
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Identify where the business, users, and affected people are located, which sector rules apply, and whether your organization is acting as a buyer, deployer, or provider of the AI system. A business that modifies, integrates, or supplies an AI system may have obligations different from those of a business using a vendor’s system. Have legal and compliance specialists map the requirements that apply to the actual use rather than assuming a general AI framework is a substitute for sector-specific rules.
The EU AI Act is one example of why scope matters. Its requirements depend on the system’s classification and the organization’s role. For high-risk AI systems within scope, Article 9 provides for a continuous lifecycle risk-management process, assessment of foreseeable risks and misuse, and testing against metrics and thresholds defined in advance and suited to the intended purpose. Do not apply that description to every AI system or assume a vendor’s statement settles whether a particular system is in scope; check the Regulation and obtain a current scope assessment.
Assess the whole supplier and its supply chain
A security questionnaire covers only part of supplier risk. NIST SP 1326, published in final form in July 2026, describes due diligence as an investigation of pertinent information about a supplier or product to inform decisions about new acquisitions and existing systems. Its ICT-focused guidance includes ownership and control, provenance, resilience, foundational cybersecurity practices, and supply-chain tiers.
Ask the vendor to identify the material providers and dependencies behind the service, such as model providers, hosting, data-processing services, and other subcontractors. Establish which parties can access data or affect service availability, what functions they perform, and how the vendor oversees them. The degree of detail needed should reflect the use case and risk; focus on dependencies that could materially affect confidentiality, integrity, availability, rights, or compliance.
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- Ownership and control: Who owns or controls the vendor and the relevant service, and could a change in control affect support, data access, or continuity?
- Provenance: What is known about the origins and handling of the relevant models, datasets, and components?
- Cyber practices: What security controls protect the service and the systems that support it, and what evidence substantiates the answers?
- Resilience: What dependencies could interrupt the service, and what continuity measures and recovery arrangements address them?
- Supply-chain tiers: Which subcontractors or other providers are material, and how will the vendor notify you about relevant changes?
Review data, confidentiality, and rights
Trace data through the full service lifecycle: what enters the system, where it goes, which parties can access it, how long it is retained, and what happens to it after the service ends. Make the review specific to the data your use case will actually expose, including personal, confidential, or regulated information where applicable.
- Which inputs, prompts, files, feedback, and usage records does the service collect or generate?
- What are the retention periods, and can the vendor use inputs or outputs to train, fine-tune, evaluate, or otherwise improve models?
- What permitted-use, confidentiality, and access restrictions apply to the vendor and its subcontractors?
- Where is data processed or stored, and how will the vendor notify you of changes relevant to your requirements?
- Who has rights to inputs, outputs, and other content, and what limits apply to use, ownership, or onward sharing?
- What information can the vendor provide about the provenance of relevant training or reference content, where that matters to the use case?
- How can you return or delete data, and how will deletion be confirmed when the relationship ends?
Do not treat broad assurances such as “your data is secure” or “you own your data” as sufficient answers. Ask for the operative terms and the details needed to understand how they apply to your intended use.
Evaluate fitness and risk for the intended use
Assess whether the system is appropriate for its particular task, not whether it performs well in the abstract. NIST identifies characteristics such as reliability, safety, security, privacy, explainability, and fairness as relevant to trustworthy AI. They are dimensions to balance in context, not a universal scorecard that every use must satisfy identically.
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Request evaluation evidence that matches the task
Ask how the vendor evaluated the exact product or configuration you expect to use, including the evaluation method, data or scenarios, limitations, and results relevant to your intended purpose. Establish whether the evidence covers the populations, languages, operating conditions, and failure modes that matter to your setting. Vendor claims without a clear method or scope are difficult to use as a basis for approval.
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- Performance and reliability: What outcomes were measured, under what conditions, and how does the system behave when inputs are incomplete, unusual, or outside its intended use?
- Safety and foreseeable misuse: What harmful outputs or misuse cases were considered, and what mitigations are available?
- Privacy and security: What risks arise from data exposure, unauthorized access, or the system’s behavior, and how are they addressed?
- Fairness and explainability: Where relevant to the decision or people affected, what has been assessed and what information can help users understand or challenge an output?
- Human oversight: What review, intervention, or escalation is possible, and what training or operating procedures will users need?
Decide which characteristics are material to the use case and set acceptance criteria before deployment. A system intended to assist a person may need different evidence and oversight from one used to automate a consequential action.
Require usable evidence and clear accountability
Evidence is part of the control system: it allows the buyer to verify claims, manage obligations, and respond when the service changes or fails. NIST’s generative AI procurement guidance recommends use-case-specific assessment and ongoing monitoring that account for privacy, security, intellectual property, third parties, and changing risks across models, APIs, fine-tunes, and embedded tools.
Request documentation proportionate to the risk and the role of the vendor. Depending on the use, this may include system descriptions, evaluation methods and limitations, security and privacy materials, relevant incident information, and records needed to operate or oversee the system. Agree in advance what logs or other records will be available, how long they are retained, and who can access them.
- Identify named owners on both sides for security, privacy, compliance, incidents, and service changes.
- Set a process for reviewing material model, API, fine-tune, subcontractor, or feature changes before or after they affect the approved use.
- Define what incidents the vendor must report, how quickly and through which channels, and what cooperation is expected during investigation and remediation.
- Where appropriate, secure evaluation, audit, or other verification rights that let you assess relevant third-party processes rather than relying solely on unsupported assurances.
- Specify what evidence will be refreshed and how often post-deployment monitoring will be reviewed.
Make the contract part of the control design
NIST’s generative AI procurement guidance recommends addressing ownership, usage rights, quality, security, and provenance in procurement terms, as well as clauses that enable evaluation of third-party processes. Translate the due-diligence findings into enforceable terms and operating responsibilities rather than leaving important answers in sales materials or informal promises.
- Permitted use and rights: Define allowed uses of data and outputs, ownership or licensing terms, confidentiality, and restrictions on training or other reuse.
- Security and service commitments: Document relevant controls, service expectations, subcontracting conditions, and notice requirements for material changes.
- Incident cooperation: Set notification, investigation, evidence-preservation, and remediation responsibilities.
- Evaluation and accountability: Specify access to relevant documentation, records, and third-party process evaluations, subject to appropriate safeguards.
- Exit and continuity: Define data return or deletion, transition assistance, and how the business can move or wind down the use safely.
Plan for failure, change, and exit
AI services and their dependencies can change, become unavailable, or behave differently after updates. Before deployment, decide how the business will respond if the vendor has an incident, the service is interrupted, a material change undermines its approval, or the system no longer meets its requirements.
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- Identify critical functions and dependencies. Determine which business processes rely on the service and what impact an interruption or degraded output would have.
- Choose a fallback. Define whether staff will switch to a manual process, another approved service, or a paused workflow, and who can authorize that switch.
- Set change and incident triggers. Decide which vendor notices, incidents, performance signals, or regulatory developments require reassessment or suspension.
- Rehearse response. Test the relevant incident and continuity steps with the people responsible, including how to preserve records and communicate decisions.
- Make exit executable. Confirm the contract, data-handling process, transition support, and operational plan allow the organization to end or replace the service without losing necessary control of its records or critical work.
Use a structured decision record
A defensible comparison should show why a vendor was selected for this use, what evidence supports the decision, and which risks remain. NIST’s AI Risk Management Framework (AI RMF) organizes work through four functions—Govern, Map, Measure, and Manage—which can help structure the review. The AI RMF is voluntary guidance, not a legal certification; NIST is updating it, so check the current status when using it.
| Decision-record field | What to capture |
|---|---|
| Use and scope | Intended purpose, users and affected people, data, jurisdictions, foreseeable misuse, applicable requirements, and organizational role. |
| Assessment criteria | The factors material to this use, such as performance, security, privacy, rights, oversight, resilience, and continuity, with defined acceptance conditions where appropriate. |
| Evidence reviewed | Vendor answers and supporting documents, their scope and date, and any limits on what they establish. |
| Risk ownership | The accountable business owner and the responsible legal, compliance, privacy, security, procurement, or risk reviewers. |
| Open issues and mitigations | Unresolved risks, agreed controls, action owners, deadlines, and any conditions that must be met before use. |
| Approval and monitoring | The decision, its rationale, approval conditions, review triggers, and the plan for reassessment after deployment. |
Use consistent criteria when comparing vendors, but do not turn the result into an unexplained single score. Record trade-offs and approval conditions so decision-makers can see which evidence supports the choice and which risks they are accepting.
What to ask an AI vendor before signing
Use these questions in interviews and written requests, then verify important answers against contracts and evidence:
- What exactly is the product or configuration we would use, and which models, APIs, fine-tunes, tools, and subcontractors support it?
- How were the system’s performance and limitations evaluated for this type of use, and what evidence can you share?
- What data do you collect, retain, or reuse, and which parties can access or process it?
- What are the rights and restrictions for our inputs, outputs, and content, including any training or model-improvement use?
- How will you notify us about material service, model, or supply-chain changes?
- What records, logs, incident information, and third-party process evaluations will be available to us?
- What happens during an incident or outage, and what support do you provide for investigation, recovery, and transition?
- Which contract terms address our requirements, and which requested commitments are you unable or unwilling to make?
Compare the answers against the requirements established for the specific use. An answer that cannot be substantiated, a significant dependency the vendor will not explain, or a contractual gap affecting a critical control should be recorded as an unresolved risk—not silently treated as a pass.
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