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Choose an AI provider by evaluating the specific system, deployment and responsibilities—not by relying on a vendor’s general compliance claims. Start with where and how you will use the AI, who may be affected, what data it will process and how much human oversight it will have. Then identify the rules that apply and ask each candidate for evidence tied to those requirements. A framework alignment, certificate or provider statement may be useful evidence, but none alone establishes that your deployment complies with the law.
Why regulatory risk depends on the deployment
The same model or service can create different regulatory questions depending on its intended purpose, users, location, sector, data and consequences of error. Your organization’s role also matters: the party supplying a model, the party building a system around it and the organization putting that system into use may have different responsibilities.
That is why a provider’s broad claim to be “compliant” is not enough to decide whether the service is suitable for your use. First define the proposed deployment; then assess the relevant law, roles and evidence. This article provides a procurement method, not a legal determination for a particular organization.
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Record the system’s purpose, intended users, affected people, deployment countries, sector, data types, degree of automation and likely consequences if it produces an incorrect or unsuitable result. Include the surrounding application and workflow, not just the model or API. These facts help identify which legal categories and duties may apply.
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Map the people and organizations involved
For the actual arrangement, determine who supplies the model or service, who configures or integrates it, who makes it available and who uses or oversees it. Do not assume that the model API supplier takes on every responsibility for a downstream application. Check the applicable definitions and obligations against the system’s real function and contract, with qualified legal advice where the consequences warrant it.
How to turn applicable rules into provider questions
Once you have identified the relevant jurisdictions and obligations, translate each one into a request for evidence. The European Union’s AI Act is a risk-based framework for AI developers and deployers; whether a system is high-risk and which obligations apply depend on the context, the actors and the current legal text. The Commission’s official guidance and the consolidated Regulation (EU) 2024/1689 are useful starting points, but guidance and implementation details can change.
For potentially high-risk systems, the AI Act’s provider provisions include responsibilities such as quality management, technical documentation, record-keeping and, where required, conformity assessment before the system is placed on the market. The exact duties depend on classification, role and product context. Ask the provider to identify which requirements it believes apply to its part of the arrangement and to explain the basis for that position; verify the legal analysis independently.
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Use the same evaluation axes for every candidate
| Evaluation area | Questions to ask | Evidence to request |
|---|---|---|
| Regulatory fit and roles | What role does the provider perform for this system? Which obligations does it consider relevant to the intended use and jurisdictions? | A role-and-scope explanation tied to the product, deployment and applicable requirements; relevant conformity information where applicable. |
| Documentation and auditability | What records, technical material and change information can the provider supply, and under what access terms? | Relevant technical documentation, records or logs, version and change history, and an explanation of any access limits. |
| Risk and quality controls | How are risks identified, validated, monitored and addressed? What happens when a control fails or the system changes? | Documented processes for risk management, validation, monitoring, corrective action and change management. |
| Model transparency for integration | For a general-purpose model, what tasks is it intended for, what are its capabilities and limitations, and what integration conditions matter? | Information about intended tasks, capabilities, limitations, input and output specifications, and integration requirements. |
| Operational responsibility | Who monitors the system, handles incidents, oversees users, updates the model and retains records? | Clear allocation of responsibilities in the contract and operating procedures, including escalation and handoffs. |
| Data and jurisdiction | Which laws may apply to the data and service? What location, retention and processing terms are actually offered? | Contractual terms and service documentation addressing the data and processing arrangements relevant to the use case. |
| Evidence quality | Can the claim be independently checked? Does it cover this product, version, date and use? | Documents with a clear scope, date, version, covered product and stated exceptions; details of independent assurance or controlled review where available. |
These axes are a practical way to organize procurement questions, not a verbatim regulator checklist. Give each one weight according to the deployment’s actual risks and applicable duties. Treat a document as evidence only for what it covers: for example, a statement about one product or version should not be assumed to cover another.
Ask what a provider cannot disclose
Some material may be confidential or security-sensitive. If a provider cannot share it directly, ask whether a suitable independent assurance report, controlled review or other verifiable evidence is available. Record what was withheld, the reason given, any alternative evidence and the residual uncertainty. A refusal or a summary statement should not be treated as proof that an obligation is met.
A practical evaluation sequence
- Define the deployment. Document purpose, users, affected people, geography, sector, data, autonomy and consequences of error. Include the application, workflow and human oversight around the AI.
- Identify the parties’ roles. Map your organization’s and the provider’s roles for this specific system and arrangement. Check the contract and how the service will actually be used rather than assuming a supplier’s role from the fact that it provides a model.
- Determine which rules may apply. Review applicable law and sector requirements for the identified jurisdictions. For an EU deployment, consult the current AI Act text and Commission guidance; get qualified legal review for consequential or uncertain classifications.
- Build an evidence request from each duty. Ask for relevant documentation, process descriptions, logs, testing and monitoring information, change controls and contractual commitments. Request only material that relates to the intended product and use.
- Compare candidates consistently. Send each candidate the same questions and record evidence, gaps, exclusions, assumptions and access restrictions. Distinguish independently verifiable material from self-attestation.
- Test operating responsibilities. Work through who monitors performance, responds to incidents, oversees users, updates the system and preserves records. Put the agreed division of work in both the contract and operational procedures.
- Set reassessment triggers. Revisit the assessment if the system, model, provider, contract, deployment context or relevant legal guidance changes. Assign an owner for monitoring those changes.
What to check for EU AI Act scope and timing
The European Commission describes the AI Act as a risk-based framework, so classification and obligations are context-dependent. Use the current official text and guidance to check the intended purpose, relevant actor roles and applicable provisions; a provider’s characterization is an input, not a substitute for your own legal assessment.
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The Commission’s high-risk provider and deployer guidance page describes draft guidance and an updated timeline following political agreement on the AI Omnibus. As that page states it, certain high-risk rules apply from 2 December 2027 and certain systems integrated into regulated products from 2 August 2028. Those dates are tied to that guidance page and its stated context; verify the current legislation, guidance and implementation status before making a procurement or launch decision.
High-risk provider evidence
Where the relevant provisions apply, the official AI Act text and the Commission’s Article 16 service-desk material describe provider responsibilities that include quality management, documentation, logs, conformity assessment where required, and corrective or compliance duties. Ask what applies to the provider’s role and product, what evidence it can supply, and how changes or incidents are handled. Do not infer that every listed duty applies to every AI service.
General-purpose AI model information
General-purpose AI model providers have a distinct set of obligations whose application depends on the model and provider circumstances. The Commission’s guidance describes, where applicable, technical documentation for authorities and downstream system providers, information about capabilities and limitations to support integration, copyright-policy and training-content-summary duties, and an authorized representative requirement for providers outside the EU. Check scope and exceptions rather than extending these requirements to every AI service.
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For a downstream integrator, model information about intended tasks, capabilities, limitations and integration requirements can be important to understanding what the model can and cannot support. Ask for material specific to the model and version being considered, and establish how updates will be communicated.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How NIST AI RMF can help—and what it cannot establish
NIST’s AI Risk Management Framework is voluntary. NIST presents it as a resource for incorporating trustworthiness considerations into AI design, development, use and evaluation, and also provides a Generative AI Profile (NIST AI 600-1, 2024). These resources can help structure internal risk work and organize provider questions, but using them does not determine whether a law applies or replace a legal analysis.
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How to make the final selection defensible
Choose the provider whose documented capabilities, evidence, contractual commitments and operating model fit the specific deployment and its applicable requirements. A provider that gives a narrower but verifiable answer may be easier to assess than one making broad claims without product- and use-specific support.
Keep a decision record that links each material requirement to the evidence reviewed, the party responsible, unresolved gaps and any conditions for proceeding. If a gap cannot be resolved, decide explicitly whether the deployment can be changed, whether additional controls are needed, or whether the remaining uncertainty is unacceptable. Reassess when material facts or legal guidance change, rather than treating the initial procurement review as permanent.
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