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Start with the work task, not the model name
A model is only one part of an AI-enabled service. The surrounding app, integrations, settings, users, and decisions all affect its risks. Describe the proposed use before comparing products:
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- Task: What work should the service help with, and what is outside its intended use?
- Users and inputs: Who will use it, and what prompts, files, or other information will they provide?
- Downstream decisions: How will people use the output, and could it influence decisions about customers, employees, finances, safety, or legal obligations?
- Consequences of error: What could go wrong if an answer is false, incomplete, biased, or misleading?
These details determine what level of testing and oversight is appropriate. The voluntary NIST AI Risk Management Framework treats risk management as relevant across AI design, development, deployment, use, and evaluation; its scope is the system and its context, rather than a model label in isolation.
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Find out what happens to your data
Get specific answers from the provider’s current terms, settings, and documentation before submitting confidential or personal information. Check how the service treats each type of information your workflow may expose:
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- Collection and processing: Are prompts, uploaded files, generated outputs, account details, or usage telemetry processed or logged?
- Retention and location: How long is each category kept, where is it stored or processed, and can your organization choose or limit this?
- Training and improvement: Is submitted content used to train or improve models or services? Does the answer vary by account type, setting, or product feature?
- Sharing: Which subprocessors or connected services may receive the data, and under what conditions?
- Controls: Who can access the data, how is it protected, and how can your organization request access or deletion?
Do not assume that a provider’s consumer, business, and enterprise offerings have identical data terms. Verify the exact service and configuration being considered. NIST’s Generative Artificial Intelligence Profile identifies data protection and retention as risk-control considerations and notes that third-party integrations can introduce privacy and information-security risks.
Review security and vendor due diligence
Use your organization’s existing procurement and third-party risk process where applicable. Assess the service in its deployment context, including integrations, user permissions, and the kinds of information it can reach. Relevant checks include:
- How access is granted, managed, and removed, including controls appropriate to the sensitivity of the workflow.
- What security practices and supporting documentation the provider makes available, and whether those materials meet your organization’s requirements.
- Whether relevant artifacts—such as a software bill of materials, service-level agreement, or attestation report—are available and useful for the service being assessed.
- Which threat scenarios matter: who could access or attack the model or connected service, at what stage, and whether likely threats are passive or active.
- Whether data crosses national borders and what that means for your organization’s obligations and risk tolerance.
NIST recommends adapting existing third-party due diligence to AI systems. The OECD Framework for the Classification of AI Systems includes security-assessment dimensions such as attacker access, attack phase, threat type, and cross-border data flows. These are prompts for contextual assessment, not a universal pass/fail checklist.
Test the service on representative work
Set evaluation criteria before trying the service, so a persuasive demo does not become the standard of proof. Build a small, controlled set of examples that reflects the actual task and the conditions in which employees will use it.
- Define what a good result means. Choose criteria tied to the task, such as factual accuracy, completeness, consistency, usefulness, or compliance with required formats.
- Include ordinary and difficult cases. Test routine inputs, edge cases, ambiguous requests, and likely failure conditions. Use examples that reflect the range and quality of real work.
- Check errors, not just successful outputs. Look for fabricated details, omissions, inconsistent answers, inappropriate disclosures, or outputs that appear confident despite being wrong.
- Record results and limits. Note the service and configuration tested, the examples used, observed failures, and what human checking remains necessary.
- Repeat when the service or workflow changes. A different model version, integration, setting, or use case can change the results; reassess when a material change affects the workflow.
NIST recommends robust, iterative, documented testing, evaluation, validation, and verification early in the AI lifecycle. A demonstration or an unverified vendor claim does not establish dependable performance for your task. The cited guidance supplies no universal performance threshold for adoption, so the organization must decide what evidence is adequate for the specific consequences of failure.
Set rules for use and human review
Before a wider rollout, tell users how the tool may be used and who remains responsible for decisions. Make the rules concrete enough to guide everyday work:
- Identify what information employees may and may not enter.
- Specify which outputs must be checked, by whom, and before what action or decision.
- Clarify that accountability for consequential decisions remains with the designated human decision-maker.
- Give users a clear way to report errors, unexpected behavior, or incidents, and explain what happens after a report.
- Define uses that are out of scope, including repurposing the service for tasks that have not been evaluated.
NIST’s Generative AI Profile identifies acceptable-use policies and guidance for human-AI teaming as ways to reduce risks from misuse, inappropriate repurposing, and misalignment between a system and its users.
Check transparency and keep useful records
Look for disclosures that help users understand what the system does, its limitations, and how to interpret its outputs. Keep internal records sufficient to support evaluation, day-to-day operation, and incident response. Depending on the task, that may include the approved use, evaluation criteria and results, known limitations, applicable data rules, and the owner responsible for review.
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The OECD’s Framework for the Classification of AI Systems emphasizes understandable disclosures supported by robust documentation. The depth of documentation should match the system’s context and potential effects; a generic model description alone may not answer questions about the service and workflow your team will use.
Compare candidates on the same criteria
If you are choosing between services, use the same task examples and assessment criteria for each. The comparison dimensions below synthesize NIST and OECD guidance; they are not an official NIST or OECD scoring standard.
| Comparison area | What to compare |
|---|---|
| Task performance | Results on the same representative examples, including failure behavior and limitations. |
| Data handling | Collection, retention, training use, sharing with subprocessors, location, and deletion controls. |
| Security and access | Access controls, relevant security evidence, integration risks, threat scenarios, and cross-border flows where relevant. |
| Transparency and documentation | Whether disclosures and records help users and the organization understand, operate, and evaluate the service. |
| Human oversight | How much review the workflow requires, who performs it, and whether users can report problems. |
| Vendor due diligence | Whether the provider can supply information and artifacts needed for your procurement and risk process. |
There is no universal winner or adoption threshold established by the cited guidance. A service that performs well on a narrow task may still be unsuitable if its data terms, security controls, or oversight requirements do not fit the organization’s needs.
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NIST identifies AI RMF 1.0 as under revision, so consult the current NIST AI Risk Management Framework page when using the framework. NIST published its Generative AI Profile on July 26, 2024. Provider features, data terms, security controls, and model versions can change, so verify the current details for the specific service and account configuration under consideration.
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