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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteChoose an AI provider by evaluating the specific system you plan to deploy—not by ranking vendors or accepting a general claim that a product is “compliant.” Start with the use case, affected people, jurisdictions, data, and degree of automation; determine the legal roles of your organization and the provider; then request evidence for the obligations that apply. For financial-services teams, the same tool may present very different risks when used to draft internal summaries, answer customer questions, or influence a consequential decision.
Start with the deployment, not the vendor’s compliance label
Regulatory risk depends on what an AI system does, where and how it is used, who is affected, and which parties perform which functions. A provider’s website, framework mapping, or certificate may be useful evidence, but none by itself establishes that your particular deployment meets applicable law.
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Before comparing providers, write a short use-case statement that answers:
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- Purpose: What task will the system perform, and what decisions or recommendations might follow from its output?
- People affected: Will it be used by staff, customers, applicants, borrowers, investors, or another group?
- Geography and sector: Where will the system be offered and used, and which financial, privacy, consumer-protection, or other sector rules may be relevant?
- Data: What personal, financial, confidential, or otherwise sensitive information will enter the system, and what information may appear in its outputs?
- Automation and consequences: Does a person review outputs before action, and what could happen if an output is wrong, incomplete, or unavailable?
- System boundaries: Are you buying a finished application, integrating a model through an API, adapting a model, or combining several services?
These details help distinguish a low-impact assistive use from a deployment that may trigger more demanding requirements. For EU deployments, the European Commission describes the AI Act as a risk-based framework for AI developers and deployers; classification depends on the system and its context, not on a vendor’s broad product description.
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Work out who has which role and duty
Do not assume that the company supplying a model automatically carries every responsibility for an application built on top of it. Establish the roles for the actual arrangement, including whether relevant parties act as provider, deployer, importer, or another actor under the applicable rules. The contract should reflect the real division of work rather than leave it implicit.
The European Commission’s AI Act Service Desk summary of Article 16 and the consolidated AI Act text describe responsibilities for providers of high-risk systems, including quality management, documentation, logs, conformity-related steps where required, and corrective or compliance duties. Which requirements apply depends on classification, role, product context, and the current legal text. A buyer’s responsibilities are not answered simply by asking whether a supplier says it is compliant.
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For financial organizations, map the arrangement end to end: who supplies the underlying model, who configures or fine-tunes it, who operates the customer-facing service, and who decides what to do with its output. Ask counsel to review the role analysis where the use could materially affect customers or other individuals.
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Give every candidate the same questions and assess answers against the intended deployment. Record what is documented, what is contractual, what is independently verifiable, and what remains an assumption. The criteria below are a practical evaluation aid, not a verbatim regulator checklist; weight them according to the use case.
| Evaluation area | Ask the provider | Evidence to review |
|---|---|---|
| Regulatory fit and role | Which role does your organization perform for this product and deployment? Which obligations do you believe apply, and what assumptions does that view depend on? | Role descriptions, product and deployment scope, applicable conformity information where relevant, and documented exceptions or limits. |
| Documentation and auditability | What technical documents, records, logs, and change history can you provide? Who can access them, for how long, and under what contractual conditions? | Documentation tied to the offered product and version; sample records or log descriptions; retention and access terms; and a controlled review option if sensitive materials cannot be shared directly. |
| Risk and quality controls | How do you identify and manage risks, validate the system, monitor performance, address problems, and control changes? | Process descriptions, testing and monitoring information, corrective-action procedures, and change-management records relevant to the proposed use. |
| Model transparency for integration | For a general-purpose model, what are its intended tasks, capabilities, limitations, input and output specifications, and integration requirements? | Technical information adequate to assess whether the model is suitable for the downstream system, including known limitations and version-specific information. |
| Operational responsibility | Who monitors the live system, handles incidents, oversees users, applies updates, and preserves records? | Contract terms, operating procedures, escalation routes, service-change notices, and clearly assigned responsibilities. |
| Data and jurisdiction | Which locations, retention periods, and processing arrangements are actually offered for this service and data? | Applicable privacy and service terms, data-flow details, retention and deletion commitments, and any relevant subcontractor or location disclosures. |
| Evidence quality | What product, version, service, and period does each assurance cover? What exceptions or exclusions apply? | Documents whose scope and date can be checked; distinguish independent assurance from provider self-attestation and marketing statements. |
For any assurance document, check its scope, date, product or version, and exclusions. A document covering one service or deployment should not be treated as proof about another. If a provider cannot disclose sensitive material, ask whether an independent reviewer or controlled access can verify the relevant controls.
Check whether EU AI Act provisions apply
If the system is used in the European Union or otherwise falls within the Act’s scope, assess the current official text and Commission guidance against the specific system and roles. The European Commission’s high-risk guidance page describes draft guidance and an updated timeline following political agreement on the AI Omnibus. That page states that certain high-risk rules apply from 2 December 2027 and certain product-integrated systems from 2 August 2028. These dates should be verified against the current official text and implementation status before being used for planning; a draft guidance page is not a substitute for checking the law in force.
General-purpose AI models also have distinct provisions. The Commission’s guidance for general-purpose AI provider obligations describes technical documentation for authorities and downstream system providers, including information about capabilities, limitations, and integration. It also describes copyright-policy and training-content-summary duties and an authorized-representative requirement for providers outside the EU, where applicable. Do not assume that every AI service or model is subject to every one of these duties; scope and exceptions matter.
For a consequential or uncertain classification, use qualified legal review. A provider’s interpretation can inform your assessment, but it should not replace your organization’s own legal analysis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use NIST AI RMF as a governance aid, not a legal clearance
NIST describes its AI Risk Management Framework as voluntary and intended to help organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation. Its Generative AI Profile, NIST AI 600-1 (2024), offers additional governance material for generative AI. These resources can help structure internal risk work and provider questions, but framework alignment does not determine whether a law applies or establish legal compliance. NIST also notes that AI RMF 1.0 is being revised, so check the current NIST material when using it.
Run a repeatable procurement and review process
- Define the use: Document purpose, users, affected people, geography, sector, data, automation, expected consequences of error, and system boundaries.
- Map roles: Identify your organization’s and each supplier’s functions for this specific arrangement. Do not equate a model supplier with the provider of every downstream application.
- Identify applicable rules: Have legal and compliance teams assess the relevant jurisdictions and sector requirements. For EU use, consult current official AI Act text and Commission guidance; obtain qualified counsel for consequential decisions.
- Convert obligations into evidence requests: Ask for the documentation, process descriptions, logs, testing and monitoring information, change controls, and contractual commitments that address each relevant duty.
- Compare candidates consistently: Use the same questions, note gaps and assumptions, and distinguish independently verifiable material from provider statements. Where materials are restricted, ask what controlled or independent review is available.
- Test the operating model: Confirm who monitors, handles incidents, oversees human review, updates the system, and retains records. Make responsibilities workable in both contract and procedure.
- Set reassessment triggers: Revisit the assessment when the model, provider, system, data, intended use, deployment context, or relevant legal guidance changes.
Make the decision on fit and unresolved gaps
A provider is a stronger candidate when it can explain its role, supply evidence relevant to the proposed deployment, disclose meaningful limitations, and agree to operational responsibilities that match how the system will actually be used. Compare gaps explicitly: a missing document, an unclear role, or an untested incident process is a decision issue to resolve, not a detail to bury beneath a general assurance. The final choice should be specific to the use case and supported by your organization’s own legal, privacy, security, and governance review.
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