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Judge an AI company’s safety policy by looking for accountable decision-makers, system-specific risk assessments, documented testing, monitoring after launch, incident response, and ways for affected people to report problems or challenge outcomes. Then check whether the company provides evidence that these processes operate in practice and whether they fit the system’s use and applicable law. A company’s policy establishes what it says it does—not, by itself, that the controls work.
Start with the system and the policy’s scope
A broad promise to build “safe AI” is hard to evaluate unless the company says which systems and uses it covers. Begin by identifying the specific model or product, its version, where and how it is deployed, who is expected to use it, and what uses are excluded or considered high risk.
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Ask whether the policy addresses the whole system in its deployment context, rather than treating a model in isolation. A model made available through a product or API may have different risks depending on its users, connected tools, settings, and intended application.
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- Does it define intended uses, foreseeable misuse, excluded uses, and any high-risk applications?
- Does the scope include systems already in production as well as those still in development?
- Is responsibility clear when a company supplies a model but another organization deploys it?
NIST’s AI Risk Management Framework (AI RMF) includes an AI-system inventory resourced according to risk priorities. It is a useful reference for asking whether a company knows which systems it needs to govern; the framework is voluntary, not a certification. NIST AI RMF Core · NIST AI RMF overview
#1 Best Overall
Check who has authority to act
Accountability means more than naming a committee or publishing principles. The policy should make clear who identifies risk, who decides whether a system may launch, who can require changes, and who can pause or withdraw a deployment when evidence warrants it.
- Are roles and escalation paths documented, including how concerns reach senior leadership?
- Does an accountable decision-maker have authority to delay, limit, or stop deployment?
- Are staff trained for their responsibilities, and are handoffs between development, safety, legal, security, and operations defined?
- Does leadership own risk decisions, or does the policy leave responsibility diffuse?
NIST’s governance outcomes call for documented roles, communication lines, staff training, and executive responsibility for risks in development and deployment. NIST AI RMF Core
Look for risk management that continues after launch
A credible process identifies risks before deployment and revisits them as the system, its users, and available evidence change. Read for how the company considers both intended use and reasonably foreseeable misuse, what information triggers a reassessment, and how identified risks lead to specific controls.
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- Does the company describe how it identifies and prioritizes risks for each system and use?
- Does it explain what production information—such as user reports or observed failures—can prompt an updated assessment?
- Are mitigations tied to identified risks, with a way to check whether they work?
- Does the company define how a significant new risk could change access, features, deployment conditions, or availability?
For high-risk systems within its scope, Article 9 of the EU AI Act describes risk management as a continuous, iterative, documented process over the system lifecycle. It covers intended use and reasonably foreseeable misuse, post-market information, targeted risk measures, and testing against predefined metrics and thresholds. These are requirements for covered high-risk systems, not a universal checklist imposed on every AI product. EU AI Act, Article 9
Ask what the safety tests actually establish
“We test our models” is not enough to assess the strength of a policy. Look for details that let a reader understand what was tested and what conclusions can—and cannot—be drawn from the results.
- System: Which model version, product configuration, and deployment setting were evaluated?
- Scope: Which risks and use cases did the evaluation cover, and which were outside its scope?
- Method: What measures, test conditions, and thresholds were used? Were the tests repeated or updated as the system changed?
- Limits: What uncertainties, known weaknesses, or gaps does the company disclose?
- Consequences: What happens when a test finds a problem, and who has authority to require a response?
Prefer evidence tied to the actual version and deployment being considered over general statements about a model family. NIST’s AI RMF 1.0 calls for regular safety evaluations, documentation of transparency and accountability risks, tracking risks over time, and feedback and appeal mechanisms. These are evaluation prompts, not proof that any particular company meets them. NIST AI RMF 1.0
Rank #3
Trace the path from a reported problem to a response
A policy should explain how the company receives, triages, investigates, and communicates about incidents—and how lessons from them change controls. It should also give users and affected people a workable route to raise concerns. A feedback address alone does not show that reports are reviewed or can influence decisions.
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- Does the policy explain how reports are prioritized, escalated, and investigated?
- When appropriate, does the company communicate what happened to users, deployers, or other affected parties?
- Can people appeal or seek review of consequential system outcomes?
- Does the company describe how incidents and appeals inform changes to evaluation, safeguards, or deployment?
NIST’s governance materials include incident identification and information sharing; its AI RMF 1.0 also addresses feedback and appeals as part of evaluation. NIST AI RMF Core · NIST AI RMF 1.0
Find out whose input counts
Risk decisions made only within the development team can miss problems that emerge in a particular domain or affect a particular community. Look for evidence that the company gathers outside perspectives, considers them, and records what changed as a result.
- Does the company seek input from domain experts, deployers, and potentially affected communities?
- Can it explain how it evaluates and prioritizes that input?
- Does it describe instances where feedback changed system design, safeguards, or deployment decisions?
NIST’s governance outcomes call for external feedback to be collected, considered, prioritized, and integrated. The key is not simply whether consultation occurred, but whether the policy shows a route from input to a decision. NIST AI RMF Core
Compare policies on evidence, not promises
Use the same questions for each company. This comparison structure is an evaluation aid derived from NIST and EU materials; it is not an official scoring rubric published by either source. Record what the company discloses and what remains unclear rather than assigning a score that suggests more certainty than the evidence supports.
| What to compare | Evidence to look for | What an unresolved gap looks like |
|---|---|---|
| Accountability | Documented roles, escalation routes, executive ownership, and authority to pause or withdraw deployment. | The policy names principles or committees but does not say who makes risk decisions or can act on them. |
| Testing and evidence | Findings tied to a system version and deployment, with methods, measures, thresholds, limitations, and external input where relevant. | The company makes broad safety claims without explaining what was evaluated or what the evaluation found. |
| Lifecycle coverage | Processes for pre-deployment evaluation, production monitoring, incident response, feedback, and decisions about continued deployment. | The policy describes launch checks but says little about what happens as real-world evidence accumulates. |
| Transparency and recourse | Information for users and deployers, channels to report problems, and a way to challenge or review outcomes where appropriate. | People affected by a system are not told how to raise a concern or what happens after they do. |
| Legal and risk scope | Clear treatment of the company’s role, jurisdictions, system category, intended use, and the requirements relevant to that context. | The company treats one general policy as proof that every product and use complies with every applicable rule. |
Check the law that applies to the specific use
Legal obligations depend on more than whether a product is described as AI. Before treating a policy as evidence of compliance, identify the jurisdiction, the system and its intended use, and the company’s role—for example, whether it provides a system or deploys one. The EU AI Act takes a risk-based approach, so requirements differ by context and category. The European Commission’s overview identifies expectations for high-risk systems that include risk assessment, traceability, documentation, information for deployers, human oversight, robustness, cybersecurity, and accuracy. Determine applicability against the specific system and current legal text. European Commission AI Act overview
EU transparency guidance and the August 2026 date
The European Commission published guidelines on AI Act Article 50 transparency obligations on 20 July 2026 and states that the relevant obligations apply from 2 August 2026. The date concerns those transparency obligations; it should not be read as a single start date for every AI Act requirement or as proof that a particular system is covered. Check the current guidance and the system’s scope before drawing a legal conclusion. European Commission Article 50 transparency guidelines
General-purpose AI code of practice
The EU General-Purpose AI Code of Practice has Transparency, Copyright, and Safety and Security chapters. The Commission says the Safety and Security chapter applies to the small number of providers of the most advanced models subject to systemic-risk obligations. A company’s signatory status is a separate fact to verify: the Commission’s list can change, and signing a code should not be treated as a substitute for checking the relevant legal obligations. European Commission General-Purpose AI Code of Practice
Use frameworks as reference points, not proof
NIST describes the AI RMF as voluntary and says AI RMF 1.0 is being revised. It can help organize questions about governance, measurement, and ongoing risk management, but using its language—or claiming alignment with it—does not establish certification, legal compliance, or effective controls. NIST AI Risk Management Framework
Company-authored frameworks are also useful evidence of stated policy, but they are not independent verification. For example, OpenAI’s 28 May 2026 announcement describes its Frontier Governance Framework as covering risk assessment and mitigation, model reporting, security risk management, incident response, external expert input, and updates. That description shows what the company says its framework covers; it does not independently establish implementation or results.
Make a decision from the gaps that matter
After reviewing the policy and supporting material, write down the system and use you assessed, the evidence you found for each comparison area, and the unanswered questions. Focus follow-up questions on gaps that could change your decision: unclear authority to stop deployment, tests that do not match the version or setting you will use, no account of how production incidents affect controls, or no meaningful route for affected people to raise concerns.
A strong policy is specific enough to be checked, assigns real decision authority, connects evidence to action, and covers the system beyond launch. If the company’s public materials do not establish those points, treat them as undisclosed—not as proof that safeguards are absent, and not as proof that they are effective.
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