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Do not judge an AI company by its principles page alone. Evaluate evidence for the exact tool, model version, intended use, users, and deployment conditions. A credible review identifies who owns risk decisions, which harms were considered, how the system was tested, what controls reduce those risks, how incidents are handled, and how the provider can pause, repair, or retire the system.
Use the NIST AI Risk Management Framework (AI RMF) and the OECD AI Recommendation as question frameworks—not as proof that a product is safe. Then record a decision tied to your own risk tolerance and operating environment.
Define the decision before reading the vendor’s promises
Safety is contextual. An AI assistant drafting internal notes presents different risks from a model that ranks loan applicants, controls industrial equipment, or generates medical information. Start by writing down the decision you are making and the conditions in which the tool will operate.
Describe the exact system
- Product name, model name, edition, and version or release date.
- Whether the system is hosted by the provider, deployed in your environment, or connected to other services.
- Who will use it, who may be affected by its outputs, and what data it can access.
- Permitted uses, foreseeable misuse, human review requirements, and consequences of an incorrect result.
- Geography, applicable sector rules, uptime needs, and any safety-critical dependencies.
Ask the provider to identify which documentation applies to that configuration. A general statement about “our models” is weak evidence if it does not cover the model and controls you will actually use.
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Use NIST’s four-function structure to organize your review
The NIST AI RMF 1.0 is voluntary guidance for organizations that design, develop, deploy, or use AI. NIST describes it as rights-preserving, non-sector-specific, and use-case agnostic; it is not a certification or a guarantee. Its Core groups risk-management work into four functions.
| Function | What to ask the company | Evidence to seek |
|---|---|---|
| Govern | Who is accountable for risk decisions? Which policies, roles, escalation paths, and supplier controls apply? | Named accountable executives or committees, policy ownership, approval records, training requirements, and documented escalation procedures. |
| Map | What is the intended context, who can be harmed, and which legal, social, privacy, security, or operational risks are foreseeable? | System and data-flow descriptions, affected-party analysis, use and misuse cases, assumptions, dependencies, and risk registers. |
| Measure | How was the system tested, against which benchmarks or scenarios, and with what limitations? | Evaluation plans, test results, subgroup or domain coverage, red-team findings, uncertainty information, and explanations of what was not tested. |
| Manage | What controls reduce identified risks, and how are residual risks accepted, monitored, corrected, or retired? | Mitigation descriptions, release gates, monitoring thresholds, incident playbooks, rollback procedures, customer notifications, and decommissioning plans. |
The NIST AI RMF Core describes these functions as organizational practices and outcomes. Use them to expose missing evidence rather than to award a compliance badge.
“The Framework is intended to be voluntary, rights-preserving, non-sector specific, and use-case agnostic, providing flexibility to organizations of all sizes and in all sectors and throughout society to implement the approaches in the Framework.”
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This sentence appears in NIST’s AI RMF 1.0 publication, dated January 26, 2023. NIST also says the framework is being revised, so check the current NIST materials when you conduct a review.
Request evidence instead of accepting broad values statements
Send a written questionnaire and ask for current, system-specific answers. A provider may need to protect security-sensitive details, but it should still explain the control, its scope, and the evidence available under confidentiality.
- Accountability: Who can approve deployment, accept residual risk, stop a release, and authorize a rollback? Give roles and decision rights, not only department names.
- Risk record: Which harms, affected groups, data sources, dependencies, and misuse scenarios are documented for this tool? Ask when the record was last updated.
- Evaluation: What tests were run before release and after major changes? Request methodology, test population, thresholds, failure rates where shareable, and known blind spots.
- Mitigation: Which safeguards are enforced by the product, which depend on customer configuration, and which depend on human review? Ask what happens when a safeguard fails.
- Operations: How are performance drift, abuse, security events, and newly discovered harms detected? What alerts reach customers?
- Incidents: What qualifies as an incident, who investigates it, how quickly customers are notified, and how corrective actions are tracked?
- Feedback: How can users and affected people report a harmful output, challenge a decision, request correction, or appeal an automated action? Who reviews those reports?
- Change control: How are model updates, fine-tunes, new tools, and altered data sources assessed before they reach production?
Prefer dated product documentation, evaluation reports, incident summaries, model or system cards, audit findings, and contractual commitments over an undated mission statement. Check whether each artifact names the covered version and deployment conditions.
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Verify that the evidence covers your actual use
NIST’s AI RMF FAQ explains that trustworthiness considerations span pre-design, design and development, deployment, use, and test and evaluation. Your review should follow that lifecycle.
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Record the model identifier, software release, safety-filter configuration, retrieval sources, plug-ins, and other components. A test on an earlier model does not automatically establish performance for a later release. Ask how customers are notified of silent model changes and whether you can pin a version.
Intended users and affected people
Evidence from expert users may not transfer to children, non-native speakers, people with disabilities, or staff working under time pressure. Ask which populations were included, excluded, or represented only by proxies. If your use affects people who are not direct users, require a channel for those people to challenge an outcome.
Deployment conditions
Check the assumptions behind each evaluation: language, geography, network access, latency, context-window limits, data quality, human review, and downstream automation. A safeguard that works in a supervised pilot may fail when outputs are copied into an unattended workflow.
Foreseeable misuse and adverse conditions
Ask how the company tested prompt injection, data poisoning, unauthorized access, abusive requests, out-of-distribution inputs, service outages, and degraded dependencies. Request the documented residual risk when a control cannot fully prevent the scenario.
Examine safety, security, and control mechanisms
The OECD Recommendation on AI, revised May 3, 2024, calls for principles including safety, security, robustness, transparency, accountability, fairness, and respect for human rights. Apply those principles to observable product behavior.
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Safety and robustness
- What prevents dangerous, deceptive, or materially misleading outputs in normal use?
- How does the system behave with incomplete, conflicting, or adversarial input?
- Are confidence information, uncertainty, citations, or abstention available where users need them?
- Can customers set domain restrictions, approval gates, rate limits, or allowed actions?
Security and privacy
- Which prompts, files, logs, and outputs are retained, for how long, and for what purpose?
- Are customer data and feedback used for training by default, by opt-in, or not at all?
- How are credentials, connectors, tenants, and administrative actions isolated?
- How are vulnerabilities reported, triaged, patched, and communicated?
Override, repair, and shutdown
A responsible design includes a practical way to stop an unsafe action, revoke access, roll back a change, correct a known failure, and safely decommission the service. Test those controls in a pilot. Determine who can invoke them, whether they work without the vendor’s intervention, and what data or business process remains after shutdown.
Scrutinize testing and evaluation claims
“Red-teamed” or “independently evaluated” is not enough by itself. Ask what was tested, by whom, against which acceptance criteria, and what happened to failures.
Coverage
Look for tests aligned with your use case, languages, domains, user groups, and foreseeable misuse. Separate component tests from end-to-end tests that include retrieval, tools, permissions, human review, and downstream automation.
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Methods and limitations
Request the test protocol, sampling method, reference answers or adjudication rules, and uncertainty or confidence intervals when available. A benchmark score can conceal failures on rare but high-impact cases. Ask which scenarios were excluded because they were difficult to measure.
Independence and follow-through
Identify whether evaluators were internal, commissioned external specialists, customers, or regulators. Independence does not make a result conclusive, but it helps reveal conflicts. Most importantly, ask for the defect log: which findings blocked release, which received mitigations, and which risks were accepted.
Check monitoring, incident response, and remediation
Pre-release testing is a snapshot. Require an operating plan for detecting problems after launch.
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Monitoring
Ask which safety, quality, abuse, privacy, and security signals are monitored; how thresholds are set; and whether customers receive dashboards, alerts, or periodic reports. Confirm whether monitoring covers your tenant and integrations rather than only aggregate provider traffic.
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Incident handling
Obtain the incident definition, severity levels, response contacts, investigation timeline, evidence preservation process, and customer-notification triggers. Clarify how the provider handles a harmful output that is legal but violates your policy, and an outage that prevents human review.
Remediation and learning
Ask how corrective actions are assigned, verified, and closed; whether affected users are informed; and whether a fix is tested against regression. A mature process can explain how incident findings change prompts, filters, training data, access controls, documentation, or release gates.
Demand understandable transparency and a way to challenge outcomes
Users need enough information to make a safe decision without reverse-engineering the model. Request plain-language capability and limitation statements, known failure modes, data provenance summaries, update notices, and instructions for safe use.
For decisions affecting people, establish a human review path that can actually change the result. It should identify the decision, preserve relevant input and output, explain the reason for escalation, and provide a response deadline. “Contact support” is not a meaningful challenge mechanism if the person cannot reach a qualified reviewer or obtain a correction.
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Legal obligations depend on jurisdiction, the provider’s role, the model’s characteristics, and your use. The European Commission’s overview of general-purpose AI obligations under the EU AI Act describes duties for providers of general-purpose AI models and additional duties for models with systemic risk. Use current Commission guidance to determine whether a specific obligation applies; do not infer compliance from a marketing claim.
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The OECD published Responsible AI Due Diligence Guidance in 2026. It offers a due-diligence approach and points to related references, including ISO/IEC 42001 and the NIST AI RMF. Buying a standard, obtaining a certificate, or citing a framework does not by itself establish that a vendor’s particular tool is safe. Verify the scope, assessor, covered controls, and current validity.
Compare providers on identical axes
When two vendors appear similar, request the same evidence for the same use case and version. Record “not stated” rather than filling gaps with assumptions.
| Comparison axis | Questions that make the comparison fair |
|---|---|
| Scope and intended use | Does the commitment cover the exact product, model, users, geography, data, and foreseeable misuse? |
| Ownership and accountability | Are named roles empowered to approve, pause, remediate, and retire the system? |
| Risk identification | Are impacts, affected groups, dependencies, and assumptions documented and recently reviewed? |
| Evaluation | Do tests match your domain and adverse conditions, disclose limitations, and show how findings changed the release? |
| Monitoring and incidents | Are detection signals, customer alerts, response times, investigation duties, and remediation records defined? |
| Transparency and challenge | Can users understand limitations and can affected people obtain human review or correction? |
| Legal and standards context | Which obligations and standards apply to this provider role and deployment, and what is the evidence of scope? |
Recognize useful signals and red flags
Positive signals
- Documentation names a model version, publication date, owner, and deployment scope.
- Risk registers connect identified harms to specific controls, tests, and residual-risk decisions.
- Evaluation reports disclose failures, excluded scenarios, and uncertainty rather than only favorable scores.
- Customers can configure permissions, require human approval, pin versions, export logs, and disable integrations.
- Incident procedures include customer notification, root-cause analysis, regression testing, and tracked corrective actions.
- Affected people have a clear appeal or correction route with accountable human reviewers.
Warning signs
- A policy uses absolute language such as “safe” or “bias-free” without conditions or evidence.
- The provider cites a framework but cannot identify implementation owners, tests, or residual risks.
- Evidence covers a family of models but not the product version and configuration you will deploy.
- Testing is described only with a benchmark score, with no method, failure analysis, or misuse coverage.
- The vendor reserves the right to change the model without notice and offers no rollback or monitoring information.
- Support cannot explain how to report a harmful output, preserve evidence, or obtain a decision review.
Write a go-or-no-go record
Convert the review into a decision another person can audit. Record the use case, system version, evidence reviewed, open questions, required controls, residual risks, owner, approval date, and conditions that trigger a pause. Separate facts supplied by the vendor from your own assumptions and tests.
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Reassess after deployment
Set review triggers rather than relying on a calendar alone. Reopen the assessment when the model, safety filter, retrieval corpus, tool permissions, user population, jurisdiction, or business process changes; when monitoring shows drift; or when an incident reveals a new harm. Ask the provider for updated release notes and evaluations, then rerun the tests that matter to your use.
Keep an evidence trail of incidents, overrides, appeals, false positives, false negatives, and changes made in response. This record lets you determine whether controls work in practice and gives decision-makers a defensible basis for continuing, restricting, or stopping use.
Bottom line
Choose an AI tool only when its safety commitments are specific to your system and supported by accountable governance, documented risks, relevant testing, effective controls, continuous monitoring, incident response, transparent limitations, and meaningful human challenge. Frameworks from NIST and the OECD help you ask disciplined questions; they do not substitute for evidence about the tool you plan to use.
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