To assess an AI vendor, trace what happens to your data in the exact service and configuration you plan to use, verify the relevant security evidence, and make key promises enforceable in the contract. A certification or framework alignment can help organize the review, but neither proves how every product handles your data.
Start with the use case, not the vendor logo
Write down what the AI service will do, who could be affected, which data classes it will handle, and the consequences of exposure, misuse, or an outage. Identify whether the company you are evaluating is the application provider, model provider, integration layer, or a subprocessor. One product may involve several of these roles.
This supply-chain view is consistent with NIST’s supplier due-diligence guidance for information and communications technology (ICT) suppliers. NIST describes due diligence as “the investigative process of researching all available, pertinent information about a given supplier or product so that informed decisions can be made on new acquisitions or existing systems.” Its guidance includes provenance, resilience, foundational cybersecurity practices, supply-chain tiers, and foreign ownership, control, or influence where relevant. NIST SP 1326
NIST’s Generative AI Profile adds AI-specific concerns, including intellectual property, privacy, security, embedded AI dependencies, ongoing monitoring, and checking incident or vulnerability information. These are voluntary risk-management resources, not a certification that a supplier is safe for every purpose. NIST Generative AI Profile
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Map every path your data can take
Ask the vendor for a current data-flow diagram for the service and deployment you are considering. It should cover prompts, uploaded files, generated outputs, logs, telemetry, support access, backups, and any feedback or fine-tuning process. Include the model services and subprocessors downstream of the vendor.
For each category of information, get specific answers to these questions:
- What is collected, and for what stated purpose?
- Where is it processed and stored, and which staff or service providers can access it?
- How long is it retained? Does the period differ for logs, backups, or support records?
- Is it used for model training, evaluation, product improvement, or any other secondary purpose?
- What does deletion cover, when does it happen, and what happens to backups or derived artifacts?
- Which subprocessors or embedded model providers receive it, and how will you learn about changes?
Do not treat “we do not train on your data” as a complete answer until the vendor defines “your data,” the services and data types covered, the relevant account settings, and any exceptions. The same care applies to “deleted”: establish what is deleted, on what schedule, and whether copies in backups or derived materials are included.
Check whether the security evidence covers this service
Request current security and privacy materials that match the specific product, deployment, account tier, and processing involved. Depending on what the vendor has, this may include independent audit reports or certifications, scope statements, architecture and data-protection descriptions, access-control practices, vulnerability management, incident response commitments, retention and deletion controls, and subprocessor disclosures.
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Read the scope and boundaries, not just the report name. Check the reporting period, exceptions, systems included, and whether the AI service and data processing you plan to use are actually covered. A report about a vendor’s corporate environment may not establish that a particular model service, integration, or configuration is in scope.
A SOC 2 report, certification, or framework mapping can provide useful evidence about specified controls within a defined scope. It does not by itself answer whether your prompts are retained or used for training, whether privacy obligations are met, or how the model behaves. Ask for separate, direct evidence on those questions. NIST’s GAI Profile also recommends assessing suppliers against incident and vulnerability information and continuing to monitor third-party risks. NIST Generative AI Profile
Examine dependencies, provenance, and resilience
Ask who owns or controls the supplier when that is material to your risk assessment, what major models and components the service depends on, and how the vendor tracks changes to them. Find out what happens if a model or cloud provider becomes unavailable, changes its terms, or discontinues service. Ask whether there is a tested fallback and what it would mean for your data and users.
NIST’s supplier guidance identifies provenance, resilience, foundational cyber practices, and supply-chain tiers as due-diligence considerations; its GAI guidance also points to over-reliance on third-party data and the need to document fallbacks. These questions matter most when the service is central to an operation, handles sensitive information, or has few practical substitutes. NIST SP 1326 NIST Generative AI Profile
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Put data promises into the contract
Translate broad statements such as “no training” or “we delete your data” into terms that identify covered services, data, and purposes. NIST recommends that contracts and service-level agreements (SLAs) address content ownership, usage rights, quality standards, security requirements, and provenance expectations, and allow evaluation of third-party GAI processes and standards. NIST AI RMF Playbook
Depending on your use case and applicable law, negotiate clear terms for:
- Permitted uses of prompts, files, outputs, logs, and feedback, including training, evaluation, and product improvement.
- Retention periods and deletion timing and scope, including how backups and derived artifacts are handled.
- Subprocessor obligations, disclosure, and notice or approval when material providers or processing arrangements change.
- Security requirements, incident notification and cooperation, and access to relevant assurance evidence.
- Evaluation or audit rights, and what evidence the vendor will provide to demonstrate compliance.
- Export, transition, and termination mechanics, including how your data is returned or deleted.
Review standard terms for secondary-use rights, broad exceptions, unexpected liability effects, and restrictions that could make a promised audit or exit right unusable. NIST’s GAI Profile flags risks from non-standard contract terms and unauthorized secondary use of data, and recommends contingency and incident-response planning for third-party systems. NIST Generative AI Profile Have qualified counsel review obligations that depend on your jurisdiction, industry, data, or intended use.
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Use the same questions for each candidate and score answers against your use case. A simple comparison can make gaps visible; “not stated” means the vendor has not provided a comparable answer, not that the control is absent.
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| Assessment area | What to compare |
|---|---|
| Data use | Minimization, permitted purposes, training and other secondary use |
| Retention and location | Retention by data type, deletion scope and timing, processing and storage locations |
| Dependencies | Visibility into subprocessors, embedded models, and change notices |
| Assurance evidence | Recency, report period, exceptions, and whether the specific service is in scope |
| Security operations | Access controls, encryption descriptions, vulnerability management, incident practices |
| Contract rights | Audit or evaluation access, notification, cooperation, export, and termination terms |
| Resilience and fit | Fallback options and whether the service’s risks suit the intended use |
Do not assume every category deserves equal weight. NIST’s AI Risk Management Framework is voluntary, and NIST says it is being revised; check its current status before relying on it operationally. NIST also cautions that trustworthiness characteristics need to be balanced in context. NIST AI Risk Management Framework NIST AI RMF 1.0: Trustworthy and Responsible AI As NIST puts it, “The decision to commission or deploy an AI system should be based on a contextual assessment of trustworthiness characteristics and the relative risks, impacts, costs, and benefits, and informed by a broad set of interested parties.” NIST AI RMF 1.0: Trustworthy and Responsible AI
Keep reviewing after launch
Supplier due diligence is not a one-time procurement exercise. Keep an inventory of third parties with access to organizational content and set a review cadence based on the service’s risk. Reassess when a material change could alter the original decision, such as:
- A new model provider, subprocessor, or data category.
- A change to retention, training, or other data-use policy.
- A security incident, relevant vulnerability, or material audit exception.
- An acquisition, ownership change, or significant change in the intended use.
- A change in the importance of the service or the consequences of its failure.
Maintain an incident-response plan for the service, document third-party AI incidents, and test fallback arrangements rather than assuming they will work. NIST’s GAI Profile recommends continuous monitoring and contingency planning for third-party GAI systems. NIST Generative AI Profile
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