Evaluate an AI vendor against the specific way your organization will use its service—not just its certificate, questionnaire answers, or model name. Start by mapping the data the system receives, the tools and suppliers it can reach, who relies on its outputs, and the consequences of failure. Then verify relevant security and AI practices, test the deployment where risk warrants it, put requirements and evaluation rights in the contract, and keep monitoring after approval.
Start with the use case, not the vendor’s general claims
An AI service that drafts low-sensitivity internal notes presents a different exposure from one that processes confidential or regulated information, connects to business systems, or influences consequential decisions. A vendor assessment is useful only to the extent that it covers the service configuration and purpose you are considering.
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NIST’s Generative AI Profile recommends use-case-based supplier risk assessment and due diligence covering security, privacy, intellectual property, and other risks. NIST’s AI Risk Management Framework (AI RMF) is a voluntary framework intended to improve how trustworthiness considerations are incorporated into AI design, development, use, and evaluation; it is not a vendor approval certificate. NIST says the AI RMF 1.0 is being revised. NIST AI Risk Management Framework overview.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWrite down the proposed deployment
Before sending a questionnaire, describe the intended use in a short assessment record. Include:
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- Purpose and users: what task the AI performs, who will use it, and whether its outputs are suggestions, drafts, or inputs to a decision.
- Data: what content users may submit, its sensitivity and ownership, whether personal, confidential, or regulated information is in scope, and whether the service can retain or use that content.
- Deployment: the product or API, the organization’s configuration, the model or models involved where known, and any retrieval, fine-tuning, or other components.
- Connections: integrations, tools, APIs, and organizational systems the service can access, plus the permissions granted to each.
- Controls and oversight: who can use the service, how access is managed, what human review occurs, and what happens when the system returns an incorrect, unsafe, or unavailable result.
- Impact of failure: the likely consequences of data exposure, service interruption, manipulated output, poor performance, or a compromised dependency.
These details define what evidence to request and what testing to perform. A security response that addresses a vendor’s general platform but not the feature, data path, and configuration you will actually use does not settle the deployment-specific question.
Map the suppliers and components behind the service
Review the chain of organizations and components that can receive your content, operate the service, or affect its security. The contracting vendor may not be the only relevant party. Depending on the service, the map can include a model provider, cloud or hosting provider, data supplier, subcontractor, fine-tuning or retrieval component, tool, or external API.
NIST’s Generative AI Profile recommends inventorying third parties with access to organizational content and maintaining approved lists of generative-AI technologies and service providers. NIST SP 1326, Cybersecurity Supply Chain Risk Management: Due Diligence Assessment Quick-Start Guide, gives a broader supplier-diligence structure: foreign ownership, control, or influence (FOCI); provenance; resilience; foundational cyber practices; and supply-chain tiers. SP 1326 is scoped to information and communications technology suppliers, so use it as a due-diligence structure rather than claiming that it is an AI-specific certification. NIST SP 1326 final publication page.
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Ask the provider to identify the service components and supplier relationships material to your deployment. Clarify which parties can access submitted content, where processing occurs if that matters to your requirements, what functions each party performs, and whether the provider can notify you when material dependencies change. Distinguish confirmed details from items the vendor cannot disclose; a non-disclosure or lack of detail is not itself proof of a vulnerability, but it may limit your ability to judge exposure.
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Record the answers alongside your own integration map. For example, a provider’s statement that it uses a third-party model does not tell you whether your users’ prompts are sent to that provider, whether your retrieval index is involved, or what other connected tools can do. Those are deployment-specific questions to resolve before granting access to sensitive content or systems.
Request evidence that matches the risks
Use vendor questionnaires to locate evidence, not as the evidence itself. Request material that helps you assess the actual service and the practices around it. NIST’s December 2025 initial preliminary draft of the Cybersecurity Framework Profile for Artificial Intelligence recommends considering supplier trustworthiness, data transparency, training and evaluation methods, AI-specific due diligence, and adversarial testing. Because IR 8596 is an initial preliminary draft, treat it as proposed guidance, not a final standard or binding requirement. NIST IR 8596 initial preliminary draft.
Evidence to request
- Security governance and controls: relevant security policies and control evidence for the service, including how access is managed and how the provider handles security issues.
- Vulnerability and incident handling: the process for receiving and addressing vulnerability reports, responding to incidents, coordinating with customers, and communicating material events.
- Resilience and recovery: information about continuity and recovery practices relevant to the service and the consequences of its unavailability.
- Model and data scope: what model or service components are in scope, what data is processed, and the provider’s description of relevant data handling and dependencies.
- Training and evaluation: how the vendor trains and evaluates the relevant system, what methods apply to your intended use, and what limitations the vendor recognizes.
- Testing: what security and adversarial testing has been conducted, what parts of the deployed service it covered, and how findings are handled. Ask for scope and method, not only a statement that testing occurred.
- Supply-chain due diligence: how the provider addresses supplier provenance, resilience, foundational cyber practices, supply-chain tiers, and FOCI where applicable.
Match the requested evidence to the exposure you documented. For a low-impact use with no sensitive data or integrations, an exhaustive review of every upstream component may be disproportionate. For a sensitive or high-impact deployment, limited visibility into data handling, dependencies, incident cooperation, or test scope may be a material obstacle to approval. This is a risk-based decision, not a universal checklist score.
Interpret attestations carefully
A certificate, independent assessment, or completed questionnaire can support a review, but it does not by itself demonstrate how a particular AI feature behaves in your configuration. Check what service, scope, period, controls, and exclusions the evidence actually covers. If an attestation addresses a platform but not a connected model feature or integration, request the remaining deployment-specific detail rather than treating the attestation as a complete answer.
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Likewise, a broad statement such as “we use industry-standard security” is not a description of a control or a test. Ask the vendor to identify the practice, its scope, who performs it, and how it applies to the product and data path you are assessing.
Test the system in its intended context
For sensitive or higher-impact uses, plan an authorized evaluation of the deployed configuration rather than relying only on documentation. NIST IR 8596’s initial preliminary draft suggests AI-specific due diligence and adversarial testing before adopting a third-party AI solution to assess model vulnerabilities, ethical alignment, and performance impact. The recommendation is draft guidance; the appropriate test depth depends on your use case, exposure, and ability to conduct testing safely.
Build a bounded test plan
- Set scope and permission. Identify the exact tenant, product features, model configuration, integrations, test accounts, and data allowed. Obtain written authorization from the vendor for any testing that could affect systems or users.
- Test access and data exposure. Verify that roles and permissions match the intended use. Check whether users or connected tools can access content beyond their authorization, and whether information supplied in one workflow is exposed in another.
- Exercise relevant threat scenarios. Choose tests tied to the application—for example, whether untrusted input can steer a connected tool or whether a retrieval workflow exposes content outside its intended scope. Avoid treating a generic test result as proof for every product or use.
- Check failure behavior. Observe what happens when the service is unavailable, returns an uncertain or unsuitable output, or encounters unexpected input. Confirm whether human review, safe fallback, or escalation works as designed.
- Record findings and remediation. Document configuration, test conditions, observed behavior, severity, owner, and corrective action. Agree with the provider on how material findings will be addressed and verified.
Do not run intrusive tests against a vendor’s production service without authorization and a defined scope. Testing should not expose other customers’ information, disrupt service, or exceed the access you are permitted to use. If the vendor will not allow the testing needed to assess a material risk, record that as a limitation and decide whether the residual uncertainty is acceptable.
Turn review findings into procurement requirements
Security approval is not durable if the agreement leaves important data uses, evaluation limits, or incident duties ambiguous. NIST’s Generative AI Profile recommends well-defined contracts and service-level agreements covering ownership, usage rights, quality standards, security requirements, and provenance expectations. It also recommends clauses that permit the organization to evaluate third-party processes and standards. NIST states: “Include clauses in contracts which allow an organization to evaluate third-party GAI processes and standards.” — National Institute of Standards and Technology, Generative Artificial Intelligence Profile, GV-6.1-006. NIST AI 600-1, Generative Artificial Intelligence Profile.
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Address these points in the contract or service terms
- Security requirements: define the security commitments relevant to the service and the obligations to cooperate when a concern arises.
- Content ownership and permitted uses: specify who owns submitted content and outputs, what the vendor may do with them, and whether they may be used for training, evaluation, or another purpose. Do not rely on assumptions about data use.
- Provenance and disclosure: set expectations for information about model, data, and material supplier provenance that your organization needs to manage the risk.
- Evaluation rights: provide a workable right to assess relevant processes and standards, including the information or testing access needed for the deployment’s risk.
- Incident cooperation and notification: state how the parties coordinate, what information is shared, and how the vendor supports investigation and response.
- Change management: establish how material changes to models, data practices, dependencies, or service features are communicated and assessed.
- Exit and contingency: define how the organization can stop using the service, handle its content, and continue critical work if a high-risk third-party service fails or becomes unsuitable.
Translate a risk finding into a specific obligation where possible. For example, if approval depends on a restriction against using submitted content to train a model, state that permitted-use boundary in the agreement rather than relying on a sales explanation. If review depends on notification of a material supplier change, specify the notice and reassessment process. Legal, privacy, security, procurement, and the business owner should align on which conditions are prerequisites to use and which are accepted residual risks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare vendors on the same use-case-specific basis
When reviewing multiple providers, use common criteria and assess each against the same deployment description. The dimensions below synthesize NIST’s supplier, generative-AI, and supply-chain guidance; they are a practical comparison framework, not an official NIST scoring scheme.
| Comparison dimension | What to compare |
|---|---|
| Data sensitivity and access | What content enters the service, who can access it, how connected tools and systems are scoped, and what data-use terms apply. |
| Model, data, and supplier transparency | How clearly the provider describes relevant models, data handling, dependencies, supplier tiers, and provenance limits. |
| Resilience and continuity | Evidence and commitments relevant to service availability, recovery, fallback, and the impact of a third-party failure. |
| Security practices and vulnerability handling | Relevant controls, vulnerability intake and remediation, incident cooperation, and the scope of supporting evidence. |
| Quality and scope of testing | How training and evaluation are performed, what security or adversarial tests cover, and whether the evidence relates to your use and configuration. |
| Incident support | How the vendor coordinates with customers, shares information, and supports response to incidents affecting the service or customer content. |
| Contractual protections | Ownership and permitted data use, provenance expectations, security obligations, and the organization’s rights to evaluate relevant processes. |
| Ongoing monitoring | Whether the provider supports change visibility and reassessment, and whether your organization can maintain a current inventory and review cadence. |
Do not collapse these dimensions into a single score without preserving the reasons behind it. A vendor with stronger documentation may still be unsuitable if it cannot meet a critical data-use restriction or if the system’s access is too broad. Conversely, a gap in a noncritical area may be manageable through a contractual commitment, configuration change, compensating control, or restricted pilot. Record the decision, its conditions, accountable owner, and any unresolved exposure.
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Monitor the vendor and reassess when conditions change
Approval is a point in an ongoing relationship, not a permanent finding. NIST’s Generative AI Profile recommends ongoing monitoring, assessments, alerting, and dynamic risk assessments for third-party generative-AI risks. It also recommends contingency processes for failures or incidents involving high-risk third-party AI systems.
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 current publication page records an update dated November 1, 2024. NIST SP 800-161 Rev. 1 final publication page.
Set reassessment triggers
- The business purpose or consequences of the AI output change.
- The service begins processing a new category of data or receives broader system access.
- The model, product feature, hosting arrangement, or material supplier changes.
- A vulnerability, incident, or meaningful test finding affects the service or its dependencies.
- The provider changes its data-use terms, security commitments, or ability to support evaluation.
- The organization’s monitoring, incident response, or fallback arrangements no longer match the deployment.
Maintain an approved inventory of AI services and relevant third parties, with an owner for each deployment. Track material changes and incidents, revisit the evidence needed for the current risk, and confirm that fallback and exit arrangements still work. A reassessment should answer whether the original approval conditions remain true—not merely whether the vendor can complete the same questionnaire again.
Use NIST guidance with its publication status in view
| Publication | Status and date | How it informs a vendor review |
|---|---|---|
| NIST AI RMF 1.0 | Released January 26, 2023; NIST describes it as voluntary and says it is being revised. | Provides a general framework for incorporating trustworthiness considerations into AI design, development, use, and evaluation. It is not a vendor certification. |
| NIST AI 600-1, Generative AI Profile | Published July 26, 2024. | Addresses use-case supplier assessment, due diligence, contracts and evaluation clauses, third-party inventory, monitoring, and contingencies. |
| NIST SP 800-161 Rev. 1 | Current publication page records an update dated November 1, 2024. | Integrates cybersecurity supply-chain risk management into risk management activities and covers risk assessment for products and services. |
| NIST IR 8596, Cybersecurity Framework Profile for Artificial Intelligence | Initial preliminary draft dated December 2025. | Proposes AI-focused considerations including supplier trustworthiness, data transparency, training and evaluation methods, due diligence, and adversarial testing; treat as draft guidance. |
| NIST SP 1326, Cybersecurity Supply Chain Risk Management: Due Diligence Assessment Quick-Start Guide | Final publication July 8, 2026; scoped to ICT suppliers. | Organizes due diligence around FOCI, provenance, resilience, foundational cyber practices, and supply-chain tiers. |
These documents support a risk-based review, but they do not remove the need to decide what is appropriate for your organization’s use, legal obligations, and risk tolerance. Treat draft recommendations as draft, use voluntary guidance as a framework rather than a guarantee, and document why the evidence is sufficient for the particular deployment.
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