To assess an AI company’s safety claims, match each claim to a specific system and version, then look for evidence that the company assessed relevant risks, tested the system under stated conditions, acted on findings, and monitors what happens after release. A safety policy is a commitment; it is not, by itself, proof that a control was implemented or that it works.
Start with the system and safety claim
Evidence is meaningful only when you know what it covers. A broad statement such as “our models are safe” is difficult to verify unless the company identifies the model or system, version, release, deployment mode, intended use, and risks addressed.
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Pin down the scope
- Which model, product, or integrated system is covered, and which version or release was assessed?
- Was the evaluation of a standalone model, a product that uses the model, or a particular deployment with additional safeguards?
- What intended uses, users, and foreseeable misuse scenarios were considered?
- Which harms and risk categories does the claim cover—and which does it exclude?
A test of one model release does not automatically establish the safety of a later release, a different product, or every use of that model. Ask the company to state the boundary plainly.
Find the accountable owner
A policy is more actionable when it names who makes safety decisions, who is responsible for risk controls, and how issues are escalated. If responsibilities are shared across model developers, product teams, and deployers, the company should explain that division rather than imply that one policy covers every party’s work.
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Separate promises, implementation, and evidence of effectiveness
Safety language can describe different levels of assurance. Check which level the company actually supports with evidence:
- Commitment: A policy says the company intends to follow a principle or process. This shows what it has promised, not whether it did so.
- Implementation: The company identifies controls, procedures, or assigned responsibilities that are in place for the system in scope. Look for concrete descriptions or records, not just a list of aspirations.
- Effectiveness evidence: The company reports how it evaluated those controls, under what conditions, what it found, and what limits remain. Results should connect to the specific version and risks named in the claim.
Ask for evaluation protocols, test conditions, thresholds where applicable, major findings, known limitations, and resulting mitigation actions. A headline such as “passed safety testing” is hard to interpret without the scope and method: a test can only support conclusions about the scenarios it actually examined.
Read the limitations, not only the headline
Useful disclosures identify what the system can and cannot do reliably, what risks remain, and where safeguards may fail or depend on deployment conditions. Limitations are part of the evidence: a company that describes them clearly gives users a better basis for deciding whether a system suits a particular use.
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Trace important risks through mitigation and follow-up
For each material harm the company identifies, look for a connection between the risk, the measure intended to reduce it, the person or team responsible, and the company’s decision about any remaining risk. A mitigation list without that connection may not show whether the controls address the risks the company says it has considered.
Check testing and residual risk
- Does the company explain how it identified plausible harms, misuse routes, affected people, and the severity of possible consequences?
- Are evaluation methods and test conditions described well enough to understand what was and was not tested?
- Where relevant, are adversarial tests documented, with findings and limitations rather than only a statement that testing occurred?
- Does the company explain what changed after a concerning result, or why it accepted remaining risk?
NIST’s 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 about risk work, but citing or aligning with the framework is not proof that a particular system passed an independent test or that every risk is controlled.
Look beyond release day
Ask how the company tracks serious incidents, investigates them, makes corrections, and communicates material changes. Also ask how it reassesses risk when the model, data, surrounding system, or deployment changes. There is no universal update schedule established for all companies; the useful evidence is a clear process and an explanation of when reassessment is triggered.
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Check what deployers are told, and how security is handled
Safety evidence needs to reach the people making deployment decisions. For high-risk AI systems, Article 13 of the EU AI Act addresses information for deployers, including intended purpose, performance, capabilities, limitations, and foreseeable risks. When assessing a provider’s disclosures, consider whether the information is specific and usable for the system and intended purpose—not merely whether a document exists.
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Distinguish voluntary guidance from legal duties
Frameworks and laws answer different questions. NIST AI RMF is voluntary guidance. EU AI Act obligations are legally defined and depend on matters such as the system category, provider or deployer role, jurisdiction, and applicable date. A reference to either one should not be treated as a blanket certification of safety or as evidence that every company has the same obligations.
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| Reference | What it can tell you | What to verify |
|---|---|---|
| NIST AI RMF | Voluntary guidance for risk management across design, development, use, and evaluation. | Whether the company explains how it applied the guidance to the named system and what evidence supports its claims. NIST says AI RMF 1.0 is being revised. |
| EU AI Act, Article 13 | Information to deployers about specified matters for high-risk AI systems, including intended purpose, performance, capabilities, limitations, and foreseeable risks. | Whether the system and relevant provider or deployer role fall within the provision, and whether the information is meaningful for the intended use. |
| EU AI Act, Article 55 | Requirements for providers of general-purpose AI (GPAI) models with systemic risk, including model evaluation, documented adversarial testing, systemic-risk assessment and mitigation, serious-incident documentation and reporting, possible corrective measures, and cybersecurity. | Whether the model is within this category and the company is a covered provider. Do not generalize these duties to every AI company or model. |
| EU AI Act, Article 50 transparency obligations | Transparency duties addressed in European Commission guidelines published July 20, 2026. The Commission says these obligations apply from August 2, 2026. | Whether the relevant system, operator role, and obligation are covered. Transparency compliance is not, by itself, evidence that a system is safe. |
These legal references are not interchangeable. A company’s location alone does not settle whether a specific requirement applies; check the system’s classification, the party’s role, and the relevant effective date. This assessment method is not a legal opinion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check whether the policy still matches the product
Compare the date and scope of the policy or evaluation with the version currently offered. Look for a revision date, named releases, and an explanation of how the company handles material changes to models, data, system components, or deployment. If those details are missing, ask which release the evidence supports and what events would prompt a new assessment.
Because NIST reports that AI RMF 1.0 is under revision and EU AI Act guidance and obligations may change, confirm current authoritative materials and applicability for the relevant jurisdiction and system category. A policy that once described a product accurately may not cover a later version or changed use.
A practical way to make the assessment
For a specific safety claim, write down the exact claim and the system version it concerns. Then request the supporting method, findings, limitations, mitigation decisions, and follow-up process. Mark each item as documented, partly explained, or not stated; do not treat an absent disclosure as proof that no work was done, but do treat it as a limit on what an outsider can verify.
A stronger claim is one whose scope is precise, whose evidence matches that scope, whose limitations and residual risks are acknowledged, and whose owner can explain what happens when conditions change or an incident occurs. If the company offers only principles or broad assurances, the fair conclusion is that the public information does not establish whether the relevant controls were implemented or effective.
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