AI Vendor Assessment ToolA vendor review should follow what the AI tool can access, produce, and influence. The same questions do not fit every product category. Use these prompts to identify evidence gaps for a specific product, configuration, data flow, and human workflow. They do not certify a vendor or decide legal compliance.
Coding assistants
An AI coding assistant security assessment should review the editor extension, command-line agent, repository integration, and hosted model separately. Ask which files or repositories it can read, whether it can write code or run commands, what content leaves the developer environment, and whether prompts, snippets, or telemetry are retained or used for model improvement. Check permissions for source control, package registries, terminals, and issue trackers; require human review for generated changes and a way to revoke tokens.
An AI code assistant enterprise security review should verify that the proposed enterprise account, plan, and configuration match the vendor terms and evidence. Confirm whether the IDE extension and downstream services are in the assurance boundary, how administrators control access, and what happens to code and prompts. NIST’s Secure Software Development Framework provides general secure-development practices. Its publication index lists SSDF 1.1 as final and 1.2 as draft as checked on 7 October 2026. SSDF is not an AI coding-assistant standard or product certification.
Chatbots and customer support
An AI chatbot vendor evaluation should map customer-visible answers to retrieval sources and escalation paths. Ask which customer records the bot can see, whether it can call account tools, and whether an output can change a payment, access, refund, or complaint status. Keep consequential actions behind explicit authorization and verify how unsupported answers reach a person.
An AI customer support vendor assessment should clarify the human handoff, correction process, logs available to investigate a disputed answer, and the permissions required for any action. Ask how the vendor describes known limitations, data handling, output monitoring, and incident response. NIST AI 600-1, the Generative AI Profile, is voluntary risk-management guidance; it does not establish that a particular chatbot is accurate or suitable.
HR technology and recruiting
An AI vendor assessment for HR tech or AI HR tools vendor assessment should identify the hiring or employment step the tool supports: sourcing, screening, ranking, interviewing, scheduling, or performance-related decisions. Ask what applicant or employee data is used, how outputs are interpreted, who can review or override them, how a person can correct information, and what alternatives exist if the tool does not work accessibly.
An AI recruiting software risk assessment should name the specific job or workforce decision, who remains accountable, how an affected person can request correction or human review, what evidence supports the vendor’s claims, and which local employment and privacy rules need separate review. The EEOC resource on AI and the ADA discusses disability-related questions when AI or algorithmic tools assess applicants and employees. It is a specific agency resource, not a full survey of employment law.
Meeting assistants and note takers
An AI meeting assistant vendor risk review should follow audio, video, transcripts, summaries, action items, and metadata through capture, storage, model processing, search, sharing, and deletion. Confirm how participants are informed, which controls a host can set, who can retrieve recordings, whether content may be used for improvement, and how retention applies to transcripts and derived notes.
For an AI note taker security risk review, also check calendar and conferencing permissions, meeting-room access, default recording behavior, transcript export, administrator controls, and whether a participant can stop capture. Check applicable recording, privacy, and employment rules with counsel for the relevant locations.
Contact centers and fraud detection
An AI contact center vendor risk review should map call audio, transcripts, customer account data, agent-assist suggestions, quality scoring, and actions the system can initiate. Ask what is recorded, who may access it, how a supervisor corrects an output, what is retained, and how a service outage affects routing and escalation.
An AI fraud detection vendor assessment should identify the decision supported and whether a person can delay, correct, or appeal a result. Request the input types, data sources, version and change process, evaluation scope, limitations, and records available to investigate a disputed outcome. A general accuracy statement is not evidence for a particular population, product, or decision context. Keep unanswered scope questions visible.
Review model cards and evidence boundaries
An AI model card review checklist can capture the model’s stated intended use, evaluation context, limitations, performance measures, version, and intended audience. Compare those statements with the vendor’s actual product configuration and the organization’s planned use. Ask who produced the card, whether it covers the model or the integrated service, what data and conditions were evaluated, and how changes are communicated. A model card is evidence to review; it does not by itself validate a deployment or establish suitability.
Red flags and a practical decision
- The vendor describes an organization-wide policy but cannot identify the covered product, plan, model, or subprocessor.
- Actual settings or contract terms do not explain how prompts, files, transcripts, code, or customer records are handled.
- The tool has broad write, command, account, or decision permissions without a named owner, approval step, and revocation path.
- Evidence is expired, excludes the AI service, or leaves customer responsibilities and exceptions unclear.
- There is no route to report an incident, correct a record, contest an output, or restore human review.
Worked hypothetical example: A software team evaluates a coding assistant. The vendor says an enterprise plan excludes submitted code from model training, but the team has not confirmed its account is on that plan. The IDE extension requests repository write access, and the assurance summary does not identify whether the extension is in scope. The reviewer records the training term and extension coverage as unknown, assigns the product owner to confirm the contract and evidence, and limits evaluation to a non-sensitive repository with write actions disabled. This example is fictional, not a test result or a finding about a real vendor.
Frequently asked questions
Can one questionnaire cover every AI tool category?
A common intake can help, but tailor follow-up to the tool’s permissions, data, outputs, and human workflow. A coding agent and a meeting note taker expose different questions.
Does a model card prove that a product is safe for my use?
No. Review its scope and limitations, then compare them with the integrated service, configuration, and intended use.
What should an HR or recruiting review ask first?
Name the employment decision and affected people, then establish data sources, human review, correction routes, accessibility considerations, evidence scope, and applicable local review.
What if a vendor will not provide requested evidence?
Record what was requested, what was provided or withheld, why it matters, and who decides whether to limit, defer, or accept the proposed use.
For the cross-category workflow, use the AI vendor evaluation guide and the AI Vendor Security Review Checklist. Tailor each question to the tool’s actual permissions and intended use.
Updated 2026-10-08. Sources are linked on this page.