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Evaluate an AI vendor against the specific way your organization will use its service—not against a certificate, questionnaire, or model name alone. Start by mapping the data and systems involved, the suppliers that can access or affect the service, and the consequences of failure. Then verify relevant evidence, test higher-risk deployments in context, put commitments in the contract, and reassess as the system changes.
Start with the use case and the consequences of failure
A vendor cannot be judged as secure or suitable in the abstract. The same service may present very different risks when used to draft public-facing copy, process confidential documents, or influence a consequential decision. NIST’s Generative AI Profile recommends assessing third-party risk in relation to the use case. Treat that as the starting point for deciding what evidence you need and how much testing is proportionate.
Write down the deployment you are actually reviewing
Before sending a questionnaire, record the intended purpose and deployment model, the people who will use the service, and what the system will be allowed to do. Include whether staff use a hosted interface, an API, or an AI feature embedded in another service. Identify the model and service dependencies if known, along with any retrieval, fine-tuning, or tool integrations involved.
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- Access: Record which organizational systems, files, APIs, or tools the service can reach, and what permissions it receives.
- People and decisions: Note who sees the outputs, whether a human reviews them, and whether an output can trigger an action or influence a consequential decision.
- Failure impact: Describe what could happen if information is exposed, an output is unreliable, access is misused, or the service becomes unavailable.
Use this record to set the review’s scope. An assessment of a low-sensitivity drafting use does not, by itself, establish that the same vendor or configuration is appropriate for confidential or regulated data. That distinction is an application of NIST’s use-case-based assessment approach, not a universal risk rating supplied by NIST.
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Turn the record into a short set of review questions before engaging the provider: What specific service and configuration are being assessed? What information will leave your environment, and which parties may receive it? Which systems can the service access, and what can it do there? Who checks outputs before they are relied on? What business or user harm could follow from disclosure, a poor output, misuse of access, or an outage? These questions keep the assessment anchored to the deployment rather than to broad claims about AI security.
Map the supplier chain and service boundaries
Review the service as a chain of organizations and components, not just as a model brand. Identify which parties receive organizational content, operate a component, or can affect the service’s security or continuity. NIST recommends inventorying third parties with access to organizational content and maintaining lists of approved generative-AI technologies and service providers.
Identify the parties and components
Ask the vendor to describe the relevant service boundary and dependencies. Depending on the offering, the map may include the AI service provider, model provider, hosting provider, data suppliers, subcontractors, and providers of fine-tuning, retrieval, tools, or APIs. For each party, establish its role and whether it can access content, change a component, or affect availability.
Do not treat a vendor’s general answer about its corporate practices as a complete description of a particular AI feature. Tie responses to the service, configuration, and deployment you recorded. If a dependency or access path is not disclosed, record that as an unresolved point rather than assuming that it is absent.
Keep an internal inventory of the AI technology and service providers that have been approved, including the use case and boundary the approval covers. An entry for one provider should not imply approval of every feature, integration, or use of that provider’s service. When mapping a chain, distinguish parties that receive content from those that may affect the service without direct content access; both kinds of involvement may matter to the review.
Use supplier due diligence as a separate lens
NIST SP 1326, finalized July 8, 2026, frames ICT supplier due diligence around five components. Use them to organize questions about the vendor and relevant suppliers:
- Foreign ownership, control, or influence (FOCI): Consider whether ownership, control, or influence creates a risk relevant to your organization and deployment.
- Provenance: Seek information about where relevant products, services, components, and data come from and how their origins are established.
- Resilience: Examine whether the supplier and service can withstand disruption and how the organization would manage a supplier failure.
- Foundational cyber practices: Review the supplier’s underlying cybersecurity practices, not only statements about AI-specific safeguards.
- Supply-chain tiers: Consider the supplier’s own dependencies and the visibility available into relevant lower tiers.
SP 1326 is scoped to ICT suppliers. Its five-part structure is useful for organizing supplier diligence, but it should not be presented as a certification, an AI-specific score, or proof that every AI risk has been addressed. Apply the topics to relevant suppliers and components in the service chain, and note where the provider’s information does not extend to a lower tier. Limited visibility is a diligence gap to understand, not evidence that a particular problem exists.
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Request evidence that matches the deployment
Questionnaires can help identify issues, but answers are claims until you understand their scope and supporting evidence. Ask for material that relates to the particular service and use case: security policies and control evidence, vulnerability and incident processes, resilience and recovery information, and descriptions of the model, data, and dependencies in scope.
Check the scope behind each answer
For every relevant policy, assessment, or attestation, establish what service, environment, and organizational processes it covers, and whether its scope matches the AI feature being considered. Ask when it was prepared and whether material changes have occurred since. A general corporate document may be useful context, but it does not automatically establish how a specific deployment handles organizational content or integrates with your systems.
A certificate or attestation can contribute evidence about the controls within its stated scope. It does not, by itself, prove the behavior of a particular AI feature, the safety of your configuration, or the security of every supplier in the chain. Identify the evidence you still need rather than treating a document’s existence as the end of the review.
For each answer, maintain a simple evidence trail: the claim, the document or explanation supporting it, the scope and date of that evidence, and any limitation that remains. For example, if a control document covers a corporate environment but does not establish coverage of the AI feature under review, record both its value and that boundary. This makes it easier for security, privacy, procurement, and legal teams to distinguish verified facts from assumptions and to focus follow-up questions.
Ask about AI-relevant practices
Request a clear account of the model and data scope relevant to the service, its dependencies, and how training and evaluation are performed. Ask what testing has been conducted against threats relevant to your intended use and what vulnerability-handling process applies. Where a vendor makes a claim about an AI system’s properties, ask what was evaluated, under what conditions, and whether the result applies to the version and configuration under review.
Clarify whether the vendor’s description covers the model, the product feature, or the full deployed service, since these are not interchangeable scopes. Ask which data and dependencies were included in its training and evaluation account, what limitations the vendor identifies, and how it handles reported vulnerabilities. The point is not to demand a universal guarantee; it is to understand which of the provider’s processes and claims bear on your actual use and which questions still require testing or contractual treatment.
NIST’s December 2025 Cybersecurity Framework Profile for Artificial Intelligence, NIST IR 8596, is an initial preliminary draft. Its proposed material recommends considering supplier trustworthiness, data transparency, training and evaluation methods, AI-specific due diligence, and adversarial testing. These are useful review topics, but the profile is draft guidance rather than a finalized requirement.
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Test the system in the configuration you plan to use
For sensitive or higher-impact uses, document an authorized test plan before adoption. The goal is to assess the deployed configuration and its failure behavior, not just the vendor’s generic model claims. NIST IR 8596’s preliminary draft specifically suggests AI-specific due diligence and adversarial testing before adopting a third-party AI solution, to assess model vulnerabilities, ethical alignment, and performance impact.
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Agree on what may be tested, which environment and accounts are in scope, what data may be used, and how findings will be handled. Include the integrations and permissions in the proposed deployment. Keep testing within the authorization and boundaries agreed with the vendor; a test that is not authorized or does not reflect the planned configuration may be unsafe or uninformative.
Examine exposure and failure paths
Check whether the service and its integrations expose only the data and actions needed for the use case. Examine access controls, the data paths into and out of the system, and what happens when the AI produces an unsuitable output or a connected component is unavailable. Evaluate threats and performance conditions that are relevant to your deployment, and record the configuration and scope so that findings are not mistaken for a universal result about the vendor or model.
Testing does not replace supplier diligence, and a successful test is not proof that a service will remain secure after a model, integration, or vendor change. Use the findings to identify controls, restrictions, or unresolved risks that need to be addressed before approval. Make the result usable by the decision-makers: identify what was tested, the conditions under which it was tested, the gaps found, and whether any issue changes the proposed use or requires a mitigation. A test result without its configuration and boundaries can be misread as a broader assurance than it supports.
Compare vendors on common, use-case-specific criteria
When reviewing more than one provider, use the same criteria and the same intended deployment assumptions for each. The comparison below is a practical synthesis of NIST guidance, not an official NIST scoring scheme. Record evidence and gaps in each cell; do not turn unlike claims into a numeric score unless your organization has a defined scoring method.
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| Review area | What to establish | What to record |
|---|---|---|
| Data sensitivity and access | What content the service receives or retrieves, who can access it, and what systems or actions are connected. | Data classes in scope, access paths, permissions, and unanswered questions about exposure. |
| Model, data, and supplier transparency | What the vendor discloses about model and data scope, dependencies, and relevant suppliers. | Named roles and components, the detail provided, and material gaps in visibility or provenance. |
| Resilience and continuity | How the supplier addresses resilience and what happens to the use case if the service or a dependency fails. | Relevant recovery information and whether the organization has a workable fallback. |
| Security and vulnerability handling | Relevant security processes and how vulnerabilities and incidents are handled for the service. | Evidence within scope, handling process, and any uncertainty about coverage. |
| Testing quality and scope | How training and evaluation are performed and what relevant adversarial or other testing has been conducted. | Test scope, conditions, configuration, and relevance to the proposed use. |
| Incident support | How the vendor will cooperate with the organization during an incident and support response. | Agreed responsibilities, communication expectations, and unresolved coordination needs. |
| Contractual rights and permitted use | What the contract says about content ownership, data use, provenance, security, and evaluation rights. | Binding terms, exceptions, and any gap between operational claims and contract language. |
| Ongoing monitoring | How changes, vulnerabilities, incidents, and evolving third-party risks will be monitored. | Review triggers, responsible owners, and the means to revisit the decision. |
Use the comparison to make trade-offs visible, not to produce a score that hides them. One provider may offer more detail about a supplier chain, while another may provide clearer testing evidence or stronger contractual evaluation rights. Whether either difference matters depends on your use case and the consequences you documented at the start. Record the evidence supporting each comparison, note missing information, and identify who must resolve or accept a material gap.
Put security and evaluation expectations in the agreement
Important assurances should be reflected in contracts or service-level terms, not left only in sales material or questionnaire responses. NIST’s Generative AI Profile recommends well-defined contracts and SLAs covering ownership, usage rights, quality standards, security requirements, and provenance expectations. It also recommends contract clauses that permit an organization to evaluate third-party processes and standards.
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NIST states in GV-6.1-006: “Include clauses in contracts which allow an organization to evaluate third-party GAI processes and standards.”
Work with procurement, security, privacy, and legal teams to address, as relevant to the deployment:
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- Security and provenance: Applicable security requirements and the provenance expectations the supplier must meet or communicate.
- Evaluation: Rights and practical arrangements for evaluating the supplier’s relevant processes and standards.
- Incidents: Cooperation and notification arrangements needed to support the organization’s response.
- Quality and service expectations: Relevant quality standards and service-level terms for the agreed use.
- Failure and exit: Contingency processes for a high-risk supplier failure or incident, including a workable fallback or exit path for the use case.
Check that the agreement describes the service and commitments being approved. A contractual promise that is vague, qualified by broad exceptions, or detached from the actual AI feature may not resolve the concern it appears to address. Evaluation rights should be practical enough to use for the relevant provider processes and standards; consider what information or cooperation the organization would need to exercise them. Likewise, incident and contingency terms should map to the roles in the supplier chain, not assume that the primary vendor alone controls every dependency.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep the decision current after approval
Vendor review is not a one-time gate. NIST’s Generative AI Profile recommends ongoing monitoring, assessments, alerting, and dynamic risk assessments for third-party generative-AI risks. NIST SP 800-161 Rev. 1 addresses cybersecurity risks throughout the supply chain and integrates supply-chain risk management into broader risk-management activities.
Set review triggers for changes that could alter the original risk assessment: a change in use case, data, model, integrations, relevant vendor or supplier arrangements, or a security incident. Track relevant vulnerabilities and incidents, and confirm that response and fallback arrangements remain usable. If the service changes materially, compare the new configuration with the scope of the original evidence and testing instead of assuming that prior approval still applies.
Assign responsibility for watching those triggers and for updating the service-provider inventory and decision record. Revisit the review when a newly disclosed dependency, a revised model or integration, or an incident changes what the service can access or how the organization relies on it. Monitoring is useful only if an alert or material change can lead to a reassessment, a restriction on use, or activation of the agreed contingency. For higher-risk third-party systems, confirm that the fallback or exit process is feasible before an incident forces the decision.
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NIST materials provide frameworks and recommendations for organizing risk decisions; they do not make a vendor secure by association or replace your organization’s approval process. Keep each publication’s status and scope clear when using it:
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- NIST AI Risk Management Framework (AI RMF): AI RMF 1.0 was released January 26, 2023. NIST describes the framework as voluntary and says it is being revised.
- NIST AI 600-1, Generative Artificial Intelligence Profile: Published July 26, 2024; includes supplier assessment, due diligence, contract, inventory, monitoring, and contingency recommendations.
- NIST SP 1326, Cybersecurity Supply Chain Risk Management: Due Diligence Assessment Quick-Start Guide: Final publication dated July 8, 2026; presents the five supplier due-diligence components described above and is scoped to ICT suppliers.
- NIST SP 800-161 Rev. 1 integrates cybersecurity supply-chain risk management into risk management activities and addresses risk assessments for products and services; its publication page records an update dated November 1, 2024.
- NIST IR 8596, Cybersecurity Framework Profile for Artificial Intelligence: An initial preliminary draft dated December 2025. Treat its proposed AI-profile material as draft guidance.
Use these materials according to their stated purpose and publication status. The AI RMF is voluntary, SP 1326 provides supplier due-diligence structure for ICT suppliers, and the AI profile in IR 8596 remains a preliminary draft. Treating them distinctly prevents a recommendation or draft topic from being mistaken for a binding requirement or a vendor-specific assurance.
Frequently Asked Questions
Is an AI vendor’s security certificate enough to approve the service?
No. A certificate or attestation can be useful evidence for the controls within its stated scope, but it does not by itself establish how a particular AI feature behaves, whether your configuration is appropriate, or whether every relevant supplier is covered. Check the scope and date, then identify evidence and testing still needed for your use case.
What should an organization ask an AI vendor before adoption?
Start with the deployment: what data the service receives or retrieves, which systems and tools it can reach, who uses its outputs, and what failure could cause. Then ask about relevant suppliers and dependencies, model and data scope, security and vulnerability processes, resilience, training and evaluation methods, testing, incident support, and contractual evaluation rights.
How should a company assess an AI vendor’s supply chain?
Map the model provider, hosting provider, data suppliers, subcontractors, and fine-tuning, retrieval, tool, or API components that can receive content or affect the service. NIST SP 1326 organizes ICT supplier due diligence around foreign ownership, control, or influence; provenance; resilience; foundational cyber practices; and supply-chain tiers.
How often should an AI vendor assessment be revisited?
Do not treat approval as permanent. NIST’s Generative AI Profile recommends ongoing monitoring, assessments, alerting, and dynamic risk assessments for third-party generative-AI risks. Revisit the assessment when the use case, data, model, integrations, supplier arrangements, or security situation changes.
Is NIST IR 8596 a finalized AI security requirement?
No. NIST IR 8596, the Cybersecurity Framework Profile for Artificial Intelligence, is an initial preliminary draft dated December 2025. Its proposed topics—including supplier trustworthiness, data transparency, training and evaluation methods, AI-specific due diligence, and adversarial testing—can inform a review, but should be identified as draft guidance.
The Bottom Line
Evaluate the service you plan to deploy, not the vendor’s reputation or a certificate in isolation. Map the data, integrations, and supplier chain; match evidence and authorized testing to the potential impact; make security, evaluation, incident, and contingency commitments contractual; and revisit the decision when the service or its context changes.
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