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Start with the use case, not the platform
“Enterprise AI” is too broad a unit for risk review. A single general-purpose model can support workflows with very different consequences, users, inputs, and oversight. Create an inventory entry for each intended use before shortlisting platforms.
- Purpose and impact: What task will the system perform, who may be affected, and what could happen if its output is wrong or unavailable?
- People and accountability: Who will use the system, who reviews its output, and which organization or organizations act as provider, deployer, or both?
- Data and workflow: What information goes in, what outputs or actions follow, and where are human review and escalation built in?
- Scope: Which countries, jurisdictions, business units, and legal or sector requirements apply?
This definition prevents a common procurement mistake: approving a platform generally, then assuming every model, integration, and workflow on it is covered by the same risk decision.
Map risk guidance separately from binding obligations
NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for managing AI risks and supporting trustworthy development and use; using it does not, by itself, establish compliance with a law. NIST describes four functions—Govern, Map, Measure, and Manage—and says AI RMF 1.0 was released on January 26, 2023. Its overview currently says version 1.0 is being revised and references an April 7, 2026 concept note for a critical infrastructure profile, so check the current framework status when setting policy. See the NIST AI RMF overview and NIST AI RMF Development.
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Use the framework as an organizing structure, then separately identify the binding requirements that apply to your organization and use case: for example, legal, sector, privacy, security, records-management, and procurement duties. An internal risk assessment or vendor assurance is not a substitute for that applicability analysis.
Check the EU AI Act by system and role
If a deployment may fall within Regulation (EU) 2024/1689, determine whether your organization is acting as a provider, a deployer, or both, and assess the specific system and circumstances. The Act has a defined scope that can include certain third-country providers or deployers when system output is used in the EU. For covered high-risk systems, the Regulation includes automatic event logging and deployer responsibilities concerning monitoring, human oversight, incident escalation, input-data relevance where the deployer controls inputs, and logs under the deployer’s control. Article 26(6) specifies retention for an appropriate period of at least six months, subject to its qualifications, including applicable law. Check the consolidated EU AI Act text against the actual system and current applicability and transition dates. Buying a platform is not proof of compliance.
Compare the exact configurations you could deploy
Ask vendors to answer for the proposed service, model, region, and contract—not just for the overall company or product family. Put shortlisted configurations through the same representative workflow and data, with success and failure criteria set in advance.
| Decision area | Questions to resolve | Evidence to request or validate |
|---|---|---|
| Jurisdiction and data handling | Where are prompts, files, outputs, and logs processed and stored? What happens to each category of data, including on deletion or service termination? | Configuration-specific processing and storage locations; retention and deletion terms; subprocessor details; applicable contractual commitments. |
| Identity and administration | Can you restrict access by role and control administrative actions for the users and environments in scope? | Identity and access-control documentation, administrative controls, and a demonstration using your intended configuration. |
| Model and change control | Which models can the workflow use, how is routing determined, and how will you know when a model, prompt, integration, or setting changes? | Model and lifecycle details for the configuration, change notices or controls, and a process for reviewing changes before they affect a regulated workflow. |
| Evaluation and safety | Can the system be evaluated against the task, population, and failure modes that matter to you? How are third-party integrations included? | Test access and records, evaluation results for your own representative workflow, and documented treatment of errors, unsafe outputs, and relevant limitations. |
| Monitoring, logs, and incidents | What events can you monitor and retrieve? Can your teams investigate an issue, escalate it, and preserve records they are responsible for? | Log scope and access, monitoring and alerting capabilities, incident and notification processes, and a practical test of retrieval and escalation. |
| Integration and exit | How does the platform fit existing security and operational systems, and what would it take to move or discontinue the workflow? | Integration and portability details, support commitments, and an exit plan covering data, logs, prompts, and dependent workflows. |
| Operations and cost | What support is available for the intended deployment, and what operational costs or dependencies affect the workflow? | Support terms and a cost estimate based on your expected use and operating model; do not compare unlike configurations. |
These are buyer-side comparison criteria, not a published ranking or benchmark of platforms. Vendor statements should be tested where practical and reconciled with the contract and your own controls.
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A platform may route work through third-party models or other generative AI components. NIST’s Generative AI Profile notes that these integrations can increase intellectual-property, privacy, and information-security risks. Its guidance calls for robust, iterative test, evaluation, validation, and verification (TEVV) practices documented across the lifecycle. Include integrations in the inventory, data-flow review, tests, and change-control process rather than treating them as invisible implementation details. See the NIST Generative AI Profile.
Validate claims at service, model, region, and contract level
A vendor’s broad statement may not describe every service or model available through it. For example, Microsoft Learn’s FAQ for Azure SRE Agent says Azure OpenAI is the default provider for EU, EFTA, and UK customers of that service, and says Anthropic models in Azure SRE Agent are not covered by Microsoft’s EU Data Boundary commitments. That disclosure applies to the named service; it is not a general statement about every Azure OpenAI offering. Review the Azure SRE Agent data residency and privacy FAQ and ask the vendor to confirm the exact commitments for your proposed configuration.
In procurement, make the vendor identify processing locations, storage, subprocessors, retention and deletion, model routing, and contractual commitments in writing. Check that this answer covers the actual workflow and all relevant data types, not merely the platform’s headline region or default model.
Use a gated selection process
- Document the use case and accountable roles. Record purpose, affected people, intended users, inputs and outputs, decision impact, human review, provider/deployer roles, and jurisdictions.
- Map risk and obligations. Use NIST’s Govern, Map, Measure, and Manage functions as voluntary structure; separately determine which binding requirements apply to the organization and use case.
- Turn requirements into pass/fail checks. Specify the evidence needed for data handling, access, model and prompt changes, evaluation, logging, monitoring, incident response, human oversight, and audit support.
- Test equivalent configurations. Run shortlisted options through the same representative workflow and data. Record results, limitations, and unresolved requirements before production approval.
- Approve conditionally and assign owners. Document the permitted use, controls, evidence, accountable teams, and conditions that would block launch or require escalation.
- Reassess after material change. Set review triggers for changes to models, prompts, data, integrations, intended use, or relevant terms. Define in advance when to require human review, suspend use, roll back, or notify affected teams.
What a sound selection decision looks like
A defensible choice is not simply the platform with the strongest demo. It is a documented match between a defined use case and a particular configuration, supported by evidence that required controls work in the organization’s workflow. Keep the risk decision tied to that scope: a change in model, region, data, integration, or purpose can change the answer.
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