Evaluate an enterprise AI vendor against the specific system you plan to deploy—not against a badge, a general privacy statement, or a promise that the product is “secure.” Define the use case, data, deployment, affected people, and every provider in the delivery chain; then verify evidence, contract terms, legal-role support, and operational safeguards against that scope. Vendor selection is the start of supplier-risk management, not its end.
1. Define the system and the use case before comparing vendors
Write down what the AI will do, which business process it supports, who will use it, and who could be affected by its outputs. Note the consequences of an incorrect answer, a data exposure, or an outage. Those details determine which risks matter and what evidence is relevant.
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Set the boundary of the service you are buying. A deployment might be a hosted model API, a complete application, a private installation, or a combination of services. Record the model and version, retrieval sources, fine-tuning, connected tools or plug-ins, integrations, human review, and deployment region. Identify every organization that can access or process your content.
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#1 Best Overall
- Purpose and users: intended task, user groups, affected people, and whether a human reviews consequential outputs.
- Data: prompts, uploaded files, personal or sensitive information, confidential business content, retrieval corpora, feedback, and generated outputs.
- Architecture: model, application, hosting, integrations, tools, storage, logging, and the organizations responsible for each.
- Operating conditions: region, access pattern, expected availability, and the effect of errors or service interruption.
Keep this system description as the scope for vendor questions and evidence review. NIST recommends maintaining inventories of third parties with access to organizational content and lists of approved providers in its Generative AI Profile.
2. Choose a control framework as a checklist, not a compliance verdict
Frameworks help organize questions and identify gaps; they do not determine whether a particular use is lawful or safe. Choose a baseline, record its version, and map its controls to the system boundary and your organization’s obligations.
| Resource | What it offers | How to use it |
|---|---|---|
| NIST AI Risk Management Framework (AI RMF) | A voluntary structure organized around Govern, Map, Measure, and Manage. NIST says AI RMF 1.0 is being revised. | Use the four functions to structure governance, context-setting, evaluation, and risk response. Record the version used and check NIST’s FAQ for status information. |
| NIST Generative AI Profile | A companion to the AI RMF, published July 26, 2024, with actions tailored to generative AI. | Use it to guide procurement diligence, third-party risk assessment, contracts, monitoring, and fallback planning. |
| CSA AI Controls Matrix v1.1 and AI-CAIQ | Released June 22, 2026, the matrix contains 247 control objectives. The related AI-CAIQ questionnaire is intended to guide self-assessment or third-party vendor evaluation. | Use its role-specific materials to organize questions for the model, application, orchestration, customer, and cloud layers. |
| OWASP GenAI Security Industry Framework Crosswalk | Dated September 1, 2026, it maps 51 GenAI vulnerabilities across four source lists to controls in 25 frameworks. | Use the crosswalk to connect AI security concerns to established frameworks, including NIST, ISO, MITRE ATLAS, and the EU AI Act. It is a mapping aid, not proof of compliance. |
NIST describes the AI RMF as voluntary guidance, not a substitute for binding law. The NIST FAQ calls the AI RMF 1.0 “a living document,” so note the framework version and check its current status when conducting an assessment.
3. Verify security evidence for the exact service and configuration
Ask for evidence that covers the product, region, model, and service tier you are considering. An assurance report or certification has a defined scope and exclusions; it does not automatically establish that your intended configuration or use is covered. Treat the following as buyer-side review questions, not as a universal mandatory certificate list.
Rank #3
- Architecture and responsibility: system diagram, trust boundaries, provider/customer responsibilities, and the locations where prompts, files, indexes, outputs, and logs are processed or stored.
- Identity and isolation: access-control options, administrative controls, tenant separation, and the design used to protect one customer’s data from another’s.
- Encryption and keys: how data is protected in transit and at rest, who controls keys, and whether the arrangement applies to the data stores and services in scope.
- Secure engineering: vulnerability reporting and remediation, patching practices, secure-development evidence, and the scope and date of penetration testing.
- Assurance scope: reports or certifications, covered services and locations, exclusions, assessment period, and any material gaps between the evidence and the purchased configuration.
- Resilience and incident handling: availability commitments, recovery arrangements, incident-response process, and the support the provider will supply during an event.
Assess ordinary information-security controls alongside AI-specific exposures. NIST describes confidentiality, integrity, and availability risks affecting AI systems and their data, as well as risks in underlying software and hardware. Its AI Security and Resilience material discusses active challenges including evasion, model extraction, and membership inference. For your architecture, consider how prompts, retrieval indexes, model artifacts, outputs, and connected tools could be exposed or manipulated.
4. Trace data use, retention, and access
Follow each data category from collection through processing, storage, support, and deletion. A statement about how a provider generally handles data may not describe the specific product, plan, region, or configuration you are buying; obtain product-specific terms and answers.
- List the flows: prompts, uploads, logs, feedback, retrieval content, fine-tuning data, abuse-monitoring data, and information accessed by support staff.
- Establish permitted purposes: whether content is retained, used for model training or product improvement, disclosed to another party, or accessed for monitoring and support.
- Pin down lifecycle controls: retention periods, deletion process, data location, access restrictions, and treatment of backups or derived data where the provider can specify them.
- Review the supply chain: subprocessors, their roles, relevant locations, and how the provider notifies customers of changes.
- Ask about rights and provenance: what rights the parties have in input content and outputs, and what information is available about the origin and permitted use of third-party data or content.
NIST’s Generative AI Profile recommends procurement due diligence that covers privacy and intellectual property, and contracts that set expectations for content ownership and usage rights. Whether those terms satisfy legal requirements depends on the jurisdictions, data, purpose, and roles involved; a vendor’s general privacy statement alone cannot establish the buyer’s compliance.
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Do not treat a vendor’s compliance claim as a transfer of your organization’s duties. Identify the applicable jurisdictions, sector rules, data categories, intended purpose, and the roles each organization performs. Have legal and privacy specialists assess the actual deployment and contract; a cross-sector procurement checklist cannot settle which clauses, transfer mechanisms, or sector-specific requirements apply.
Best Value
For an EU deployment, review the consolidated EU AI Act text against the particular system, intended purpose, actor roles, classification, exceptions, and applicable dates. The Act allocates obligations between providers and deployers. For high-risk systems, the text includes requirements such as sufficient transparency and instructions for deployers, logging capabilities, and deployer monitoring. Buyers should determine whether the vendor will provide the documentation and operational support needed for the buyer’s role; not every enterprise AI product is high risk, and applicability is case-specific.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Put evidence, cooperation, and exit rights in the contract
Sales responses are not a substitute for enforceable terms. NIST recommends using contracts and service-level agreements to set expectations for ownership, usage rights, quality, security, and provenance; it also recommends clauses that allow an organization to evaluate third-party processes and standards. Translate the risks identified in your assessment into obligations, notice periods, and remedies that fit the deployment.
- Permitted data uses, content ownership and usage rights, retention, deletion, and applicable data-location commitments.
- Security requirements, evaluation or audit rights, access to relevant assurance evidence, and cooperation with assessments.
- Subprocessor disclosure and change notification, including a defined process for raising objections or reassessing risk.
- Incident responsibilities, notification, cooperation, response times, and access to critical support.
- Service availability, material product or model changes, and how changes are communicated and assessed.
- Portability, termination, data return or deletion, and practical fallback arrangements if the service becomes unavailable or unsuitable.
- Responsibility and liability allocation that reflects the provider’s role and the buyer’s use of the system.
NIST’s Generative AI Profile also recommends planning and rehearsing third-party incident response, continuous monitoring, and fallback for third-party failures. Make sure the contract gives the teams responsible for those plans usable notification, cooperation, and support commitments.
7. Compare vendors consistently, then keep assessing the selected one
Use the same questions and evidence standard for each candidate. A weighted scorecard can make trade-offs visible, but weights should reflect the defined use case rather than a generic ranking. Avoid combining a strong control in one area with an unresolved critical gap in another into a misleading average.
- Set evaluation axes: evidence quality and control coverage; data purpose and lifecycle; model and system transparency; resilience and incident response; support for the buyer’s legal role; contract and evaluation rights; and portability and exit feasibility.
- Define what counts as evidence: distinguish contractual commitments, independent reports, technical documentation, and unverified statements. Record the scope and date of each item.
- Score with explicit criteria: define what constitutes adequate, partial, or inadequate support for each axis before reviewing vendors. Apply the criteria consistently; do not invent a universal weighting.
- Record exceptions and ownership: capture assumptions, unresolved questions, residual risks, accountable owners, and remediation dates alongside each score.
- Make the decision conditional where needed: set required contract changes, technical controls, or remediation milestones as procurement conditions rather than treating them as completed safeguards.
- Reassess in operation: monitor the provider and revisit the assessment periodically and after material changes to the model, service, data flows, subprocessors, deployment, or intended use.
This approach follows NIST’s emphasis on use-case-based supplier assessment and continued third-party monitoring, while using CSA’s role-specific resources to make the supplier boundary explicit.
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