Evaluate an enterprise AI vendor against the system you will actually deploy—not a generic certificate list. First map the use case, data, deployment, users, and every provider that can access or control part of the system. Then verify security and data-handling evidence, establish which legal duties apply to your organization and the vendor, negotiate operational and exit protections, and monitor the service after selection.
Start by defining the system and its boundaries
The same vendor can present very different risks depending on whether it supplies a hosted model API, a complete application, a private deployment, or one component in a multi-provider stack. Before requesting assurances, document what the proposed system will do and how it will be used.
- Purpose and impact: Identify the business process, intended outcomes, users, people affected, and consequences of an incorrect answer, disclosure, or outage.
- Data: List personal, confidential, regulated, and proprietary information that may enter prompts, files, retrieval sources, fine-tuning, feedback, logs, or support channels.
- Deployment: Record the region, integrations, human review, access model, and whether the service uses retrieval, plug-ins, tools, or customized models.
- System components: Record the model and version, application, orchestration layer, cloud environment, retrieval sources, and other providers that may handle organizational content.
Draw a data-flow and responsibility map. A deployment may involve a foundation-model provider, an orchestrated-service provider, an application provider, and a cloud provider, with different parties controlling different safeguards. The Cloud Security Alliance (CSA) AI Controls Matrix (AICM) v1.1 distinguishes these roles from the AI customer; use that role model to identify who must answer each question. CSA AI Controls Matrix v1.1
NIST recommends maintaining inventories of third parties with access to organizational content and approved-provider lists. Its procurement guidance says to update vendor due diligence for generative AI risks, including intellectual property, privacy, and security. NIST Generative AI Profile
#1 Best Overall
Choose a baseline to organize diligence
Frameworks help structure questions and reveal gaps; they do not determine whether a particular deployment complies with law. Pick a baseline that fits the use case, state its version in the assessment, and map its controls to the system boundary and provider roles you identified.
| Resource | How it helps | Important qualification |
|---|---|---|
| NIST AI Risk Management Framework (AI RMF) | Organizes voluntary AI risk management through Govern, Map, Measure, and Manage. | NIST describes the framework as voluntary and says AI RMF 1.0 is being revised. Record the version used and check NIST for updates. Its 2024 Generative AI Profile adds actions tailored to generative AI. |
| CSA AI Controls Matrix v1.1 and AI-CAIQ | Provides role-specific control material and a questionnaire for self-assessment or third-party evaluation. | CSA’s v1.1, released June 22, 2026, contains 247 control objectives. A control matrix supports evaluation; it is not proof that a vendor meets every requirement. |
| OWASP GenAI Security Industry Framework Crosswalk | Connects GenAI security vulnerabilities to controls in established frameworks, including NIST, ISO, MITRE ATLAS, and the EU AI Act. | The crosswalk page, dated September 1, 2026, maps 51 vulnerabilities across four source lists to controls in 25 frameworks. It is a mapping aid, not a compliance guarantee. |
Use the resources as complementary lenses: the AI RMF helps organize risk management, AICM helps assess controls by role, and the OWASP crosswalk can help connect AI-specific threats to existing control programs. None replaces legal analysis for your jurisdiction, sector, data, or use.
Verify security evidence for the service you will buy
Ask for evidence that applies to the specific product, region, model, and service tier under review. A general company-level assurance may not cover the configuration or component you intend to use. Request the scope, exclusions, dates, and covered services for each report or certification.
Ask for concrete artifacts
- System architecture, data-flow diagrams, and a responsibility matrix covering the vendor and its subprocessors.
- Identity and access controls, tenant isolation, encryption, and key-management descriptions.
- Secure development, vulnerability disclosure, remediation, patching, and software-supply-chain practices.
- Penetration-test scope and date, plus relevant assurance reports and their limitations.
- Incident response procedures, resilience measures, availability commitments, and support arrangements.
Assess AI-specific attack surfaces
Apply ordinary confidentiality, integrity, and availability review to prompts, uploads, retrieval indexes, logs, outputs, model artifacts, and connected tools. Also ask how the provider evaluates threats particular to AI, such as evasion, model extraction, and membership inference. NIST describes AI systems as having complex attack surfaces and treats these threats as active security challenges. NIST: AI Research—Security and Resilience
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsA certification or independent assessment is evidence with a defined scope, not a substitute for checking the intended deployment. The buyer-side requests above are diligence practices; they do not establish a universal certificate or test that every AI vendor must have.
Trace data use, retention, and rights
Follow each category of information through the complete service, including downstream providers and operational processes. Ask the vendor to describe the data path and answer in contractually meaningful terms, not only in a general privacy statement.
Rank #3
- Inputs and outputs: Are prompts, uploaded files, generated outputs, and user feedback stored? For what purposes, and for how long?
- Training and product improvement: Can customer content be used to train or improve models or services? Which settings or contract terms govern that use?
- Retrieval and customization: Who can access retrieval corpora or fine-tuning inputs, and how are they handled when the service is changed or ended?
- Operations: What data appears in abuse monitoring, logs, support workflows, or incident investigations? Can vendor staff or subprocessors access it?
- Location and deletion: Where is data processed or stored, what retention and deletion rules apply, and how are deletion requests handled across the service chain?
- Provenance and rights: What information can the vendor provide about the provenance and permitted use of relevant data, and who owns or may use customer content and outputs?
NIST recommends procurement diligence for privacy and intellectual property and contractual clarity on content ownership and usage rights. A vendor’s privacy terms alone do not establish your organization’s legal compliance: applicability depends on jurisdiction, data, purpose, processing roles, and the actual configuration. NIST Generative AI Profile
Map legal duties to the vendor’s and your roles
Determine the applicable obligations from the deployment’s location, intended purpose, system classification, actors, and dates. Do not assume that a vendor’s certification or compliance statement transfers the customer’s responsibilities, or that every enterprise AI product is subject to the same rules.
For deployments in the European Union
Review the EU AI Act’s role-specific provisions for the particular system and use. The consolidated text includes high-risk system requirements such as sufficient transparency and instructions for deployers, logging capabilities, and deployer monitoring. Whether those provisions apply depends on classification, intended purpose, actor role, exceptions, and applicable dates. Confirm what documentation and operational support the vendor will provide for the obligations attached to your role. EU AI Act consolidated text
For broader risk management
The NIST AI RMF is voluntary guidance, not a substitute for binding law. Use it to organize risk decisions, then have the appropriate legal, privacy, and compliance teams map actual obligations to the deployment. NIST AI RMF
Put operating, change, and exit protections in the contract
Security and privacy controls can change after procurement. Align the contract and service-level agreement (SLA) with the system boundary and evidence reviewed. NIST recommends contractual expectations for content ownership, usage rights, quality, security, and provenance, along with rights to evaluate third-party processes and standards. NIST Generative AI Profile
Negotiate terms that address the following where relevant to the use case:
Best Value
- Permitted data uses, content ownership and usage rights, retention, deletion, and applicable locations.
- Security requirements, assessment or audit rights, access to relevant evidence, and cooperation with your reviews.
- Subprocessor disclosure and notification of material changes to providers, models, service features, or controls.
- Incident responsibilities, notification, cooperation, response times, and availability of critical support.
- Service availability, fallback arrangements, portability, termination assistance, and deletion at exit.
- Responsibility and liability allocation, including what happens if the service changes or no longer meets agreed requirements.
Translate those terms into an operating plan: identify incident contacts, rehearse the response path, monitor the provider, and document a fallback for service disruption or unacceptable changes. NIST recommends third-party incident-response planning, continuous monitoring, fallback planning, and contract provisions covering incident responsibility and support.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare vendors with a use-case-specific scorecard
Give each vendor the same questions and evidence deadline, then compare the results against the risks and requirements of the proposed use. A simple scale can make gaps visible: 0 = no evidence or unacceptable; 1 = partial, unclear, or conditional; 2 = adequate evidence for the defined use. This is a buyer-selected scoring method, not a rating prescribed by NIST or CSA. Do not let an average score conceal a critical failure; set minimum thresholds for issues such as prohibited data use, missing legal support, or no workable incident and exit plan.
| Evaluation axis | Evidence to record | Decision question |
|---|---|---|
| Security controls and evidence | Relevant reports, scope, architecture, access controls, vulnerability handling, and AI threat coverage. | Does the evidence cover the actual service, region, and configuration? |
| Data handling and rights | Purpose, retention, deletion, location, training use, subprocessors, and ownership or usage terms. | Are data uses permitted and compatible with the deployment’s obligations? |
| System transparency | Model and version information, system boundary, retrieval or tools, limitations, and change notices. | Can your teams understand and oversee the system sufficiently for its impact? |
| Resilience and response | Availability commitments, incident process, support, recovery, and fallback plan. | Can the organization respond to an incident or disruption without losing essential operations? |
| Compliance support | Role-relevant documentation, operational cooperation, and evidence for applicable duties. | Does the vendor support—not purport to replace—your organization’s compliance work? |
| Contract and exit | Audit rights, change control, portability, termination, deletion, and liability terms. | Can you verify the service and leave or move the workload if necessary? |
For each finding, record the evidence reviewed, exceptions, assumptions, residual risk, owner, and remediation date. Reassess periodically and after material changes to the model, service, providers, data flows, or use case. This turns procurement due diligence into continuing supplier-risk management rather than a one-time approval. NIST Generative AI Profile; CSA AI Controls Matrix v1.1
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