How to Evaluate AI Vendors

Define the use and boundary

Describe the business purpose, intended users, affected people, operating context, and decisions the AI may make or influence. Draw the boundary around product edition, configuration, integrations, model providers, data sources, and human steps. Identify inputs, outputs, logs, and the people who can access them. Record what could happen if the service is wrong, unavailable, manipulated, or used outside its intended purpose.

Identify plausible harms and who could be affected. Consider confidentiality, security, reliability, harmful bias, explainability needs, safety, and operational dependence in context. Set requirements before reviewing vendor marketing. NIST AI RMF 1.0 groups voluntary outcomes under Govern, Map, Measure, and Manage. NIST notes that Core actions are not a fixed checklist or ordered sequence. See AI RMF 1.0 and NIST’s current Core resource.

Evaluate evidence, not assurances

A useful AI vendor selection checklist ties each requirement to evidence and an owner. Request proof for controls that match the data and access in scope: identity and least privilege, tenant isolation, encryption, logging, vulnerability handling, incident response, backup and recovery, privileged support access, retention and deletion, subprocessor oversight, and customer configuration responsibilities. For assurance reports, check the scope and period, exceptions, and complementary customer controls. The report title alone does not establish that the proposed service is covered. The AICPA & CIMA SOC suite overview is background; inspect the actual report before relying on it.

Map every data type and purpose through the vendor and its subprocessors. Ask whether prompts, files, outputs, telemetry, or support records are used for model training or improvement, and whether retention settings differ by model, region, or plan. For personal data, have privacy counsel determine applicable roles and contract terms. The FTC recommends written vendor security expectations, verification, data-use limits, and access controls; see its vendor security guidance.

Ask which models and downstream providers support the feature, how versions change, what evaluations or monitoring are performed, and what notice customers receive. Request intended use, limitations, known failure modes, oversight options, and fallback behavior. For generative AI, ask about risks relevant to the proposed application, such as confabulation, sensitive-data disclosure, prompt injection, and output misuse. NIST’s Generative AI Profile is voluntary guidance, not proof that a vendor meets a particular control set.

Keep evidence quality distinct from underlying risk. A polished policy may be weak evidence for a specific product boundary; an unknown answer is a follow-up item, not evidence of control failure or success. Request dated artifacts that explain scope, owner, method, exceptions, and customer responsibilities. A practical AI vendor assessment checklist also covers availability commitments, support and escalation, renewal and price changes, data portability and deletion, change notice, subcontracting, audit or evidence rights, incident notification, liability allocation, and exit assistance. Match each item to the product tier and contract; refer proposed terms to counsel.

Compare vendors with a transparent method

Define criteria and weights before comparing vendors. Use a 1–5 rating for evidence against each criterion, where 1 means a substantial gap and 5 means strong, current evidence for the reviewed scope. This can serve as an internal AI vendor selection scorecard Excel users maintain or as a comparable table in another format; the spreadsheet format does not make the method a validated benchmark. Set thresholds and hard stops in advance. Keep impact, evidence confidence, and risk treatment visible beside any numerical result.

Calculation: rating ÷ 5 × weight. In this hypothetical, model-change transparency contributes 2 ÷ 5 × 20 = 8 points. A 66/100 total does not establish acceptability. Resolve the model-change gap, assess its impact, and apply the pre-set decision rule before deciding.

Red flags and decision record

AI vendor red flags are prompts for verification, not automatic findings: a vendor cannot identify which service a claim covers; a general policy is offered instead of product evidence; data-use or retention answers conflict across documents; a model provider or subprocessor is undisclosed; exceptions or customer responsibilities are omitted; or a material question remains unanswered while production access is requested. Record the exact gap, why it matters to this use, who will follow up, and what interim boundary applies.

Keep a concise decision record another reviewer can understand: business owner, product and configuration, intended use, date, evidence reviewed and its scope, important unknowns, rationale, required controls, accountable risk owner, conditions, and next review date or trigger. Name allowed and prohibited uses. Reassess after changes to models, data, subprocessors, access, use, law, incidents, or contract.

Worked hypothetical example: A 120-person company considers an AI meeting-summary service for internal product meetings. The team maps audio, transcripts, summaries, meeting metadata, and identity records; customer calls are outside the initial scope. The vendor provides an access-control description and a SOC 2 report, but reviewers have not confirmed that the report covers the AI feature. They record an evidence gap, request the applicable system description and customer-control details, and confirm deletion and training-use terms before enabling recording. Until then, they permit a small synthetic-data evaluation only. This is a hypothetical process, not a real vendor review or test.

For a repeatable question set, see the AI Vendor Assessment Questionnaire. Procurement teams can adapt the AI RFP and procurement checklist; use the AI vendor security review checklist for a focused security pass. Tailor each to the service and decision.

Frequently asked questions

What belongs in an AI vendor evaluation checklist?

Include purpose and scope, data flows, security and privacy evidence, model and subprocessor changes, service terms, unresolved items, and decision ownership. Set criteria before comparing providers.

How should I compare two vendors with different evidence?

Compare against the same requirements and product boundary. Record missing or out-of-scope evidence as unknown, assign follow-up, and avoid treating an average score as a substitute for resolving critical gaps.

Can a scorecard select the vendor automatically?

No. A scorecard can make tradeoffs easier to inspect. Accountable reviewers still assess evidence, hard stops, impact, and whether conditions are met.

When should an evaluation be revisited?

Reopen it when the use, data, model, subprocessor, access, contract, or operating context changes, or when an incident or new evidence affects the original decision.

Updated 2026-10-07. Sources are linked on this page.