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To assess an AI company’s safety claims, match each claim to a specific model or system, release, intended use, and risk. Then look for evidence that the company has identified and tested those risks, acted on the findings, and monitors what happens after deployment. A policy or public commitment is a starting point—not proof that a safety control exists or works.
How do I assess an AI company’s safety claims?
Start by turning broad statements—such as “we build safe AI”—into claims you can check. Ask what product the statement covers, what risks it addresses, what the company did to evaluate those risks, and what changed as a result.
The NIST AI Risk Management Framework (AI RMF) offers voluntary, use-case-agnostic guidance for managing risks across AI system design, development, use, and evaluation. It can help organize questions, but citing or aligning with the framework is not the same as demonstrating that a particular system is safe. NIST says AI RMF 1.0 is under revision, so check the current framework status when using it.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteLegal requirements are a separate matter. EU AI Act duties depend on the system’s category, the organization’s role, and the relevant date; they do not apply identically to every AI company. The Commission’s transparency guidelines, published July 20, 2026, say the Article 50 transparency obligations they address apply from August 2, 2026. Confirm that a requirement applies to the system and role in question before treating it as a compliance test.
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What evidence should an AI company publish about model safety?
A useful disclosure lets a reader connect a defined system and risk to an evaluation, a result, a mitigation, and a remaining limitation. A principles page may explain the company’s intentions, but by itself it does not establish that a control was implemented or that it works under real deployment conditions.
- System and release: Identify the model or product, version or release, deployment mode, intended users, and intended purpose covered by the statement.
- Risk and affected people: Describe plausible harms, misuse pathways, who could be affected, and how the company judges severity.
- Evaluation method: Explain what was tested, how it was tested, under what conditions, and any thresholds or criteria used to judge results.
- Findings and limits: Report material findings and known limitations, including risks the evaluation did not cover or could not resolve.
- Mitigation and ownership: Connect significant risks to actions taken, residual-risk decisions, and the people or teams responsible for follow-through.
- Monitoring and change: Explain how issues are tracked after release and how the company reassesses risk when the model, data, system, or deployment changes.
For general-purpose AI models with systemic risk, Article 55 of the EU AI Act specifies duties that include standardized model evaluation, documented adversarial testing, systemic-risk assessment and mitigation, serious-incident documentation and reporting, possible corrective measures, and cybersecurity safeguards. These are requirements for covered providers, not a universal checklist imposed on every AI product. For high-risk AI systems, Article 13 addresses information for deployers, including intended purpose, performance, capabilities, limitations, and foreseeable risks.
How can I tell whether an AI safety policy is more than a promise?
Compare what the policy says with artifacts or disclosures showing that the company followed through. The more consequential the claim, the more important it is to see specific evidence rather than general assurances.
Check scope and accountability
Look for a clear boundary: which system and release the policy covers, where and how it is used, and which users or purposes are in scope. Then ask who makes safety decisions and how responsibilities are assigned. A company-wide commitment without a system-level scope is difficult to match to a particular deployment.
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Check the risk assessment
Look for identified harms and misuse cases, affected groups, exposure pathways, and an explanation of how risks were prioritized. NIST’s AI RMF can help frame risk management across design, development, use, and evaluation, but it is voluntary guidance rather than proof of a particular control or a legal certification.
Check the evaluation evidence
Ask for the evaluation protocol, test conditions, scope, criteria, major findings, and limitations. For relevant high-impact or adversarial risks, look for documented adversarial testing and an explanation of what the tests can and cannot establish. A claim that a system “passed safety testing” is hard to assess if the company does not say what was tested or what counted as passing.
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Trace each risk to a mitigation
For each material risk, look for a corresponding mitigation, an owner, and a decision about any remaining risk. If a risk is acknowledged but no action or residual-risk rationale is disclosed, the policy has not shown how that risk is being managed. Article 55 sets assessment and mitigation requirements for covered systemic-risk GPAI providers; applicability depends on the Act’s scope.
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Read limitations and deployment information
Limitations are part of safety evidence, not a footnote to ignore. Check whether deployers are told what the system is intended to do, its capabilities and performance, where it may fail, and what foreseeable risks matter in use. Article 13 addresses such information for high-risk AI systems. A disclosure that helps a deployer make informed decisions is more useful than an unqualified claim of reliability.
Look for incident handling and security
Check whether the company describes a process to track serious incidents, make corrections, and communicate material changes. For providers covered by Article 55’s systemic-risk GPAI provisions, the Act includes serious-incident documentation and reporting, possible corrective measures, and cybersecurity safeguards for the model and relevant physical infrastructure. Do not assume those specific duties apply to every provider or system.
Ask how reassessment works
Find out whether the company reassesses risk after meaningful changes to the model, training or evaluation data, surrounding system, or deployment conditions. There is no universal update schedule established by the sources cited here for all AI companies, so evaluate the company’s stated change-control process rather than assuming a fixed cadence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I use NIST guidance and EU AI Act requirements?
They answer different questions. NIST provides a voluntary structure for risk management; the EU AI Act sets legal obligations for defined categories and roles. Neither a framework reference nor a general statement of compliance, on its own, proves that a given safety measure has been implemented effectively.
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| Assessment lens | NIST AI RMF | EU AI Act |
|---|---|---|
| Nature | Voluntary risk-management guidance, intended to support trustworthiness considerations through design, development, use, and evaluation. | Legal obligations that apply according to the Act’s defined scope, system categories, operator roles, and dates. |
| What to look for | Evidence that the company uses risk-management practices and can connect them to the particular system and its lifecycle. | Whether a specific duty applies, and whether the responsible provider or deployer meets that duty. |
| Examples relevant to safety | Risk framing across the AI system lifecycle; the framework itself is not a system test result. | Article 13 deployer-facing information for high-risk systems; Article 55 duties for providers of GPAI models with systemic risk; Article 50 transparency obligations addressed by Commission guidelines published July 20, 2026, applying from August 2, 2026. |
| Applicability caution | Voluntary and use-case agnostic; NIST reports AI RMF 1.0 is under revision. | Not every AI company or product is covered in the same way. Classification, role, jurisdiction, and effective date matter. |
What should I conclude if the company publishes little evidence?
Conclude that the public information is insufficient to verify the claim—not automatically that the system is unsafe, and not that the company has no internal controls. State precisely what is missing: for example, system scope, test conditions, findings, limitations, mitigation ownership, or incident follow-up.
This is a due-diligence approach, not a legal opinion or an audit of a particular company. A public policy can establish what the company says it intends to do; stronger evidence shows what was evaluated, what the results were, what actions followed, and how the company handles risks that remain.
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