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Do not start by asking whether an AI company is “safe.” Start by asking whether a precise claim is supported by evidence that fits the model, risk, operating conditions and decision you face. A credible review identifies the exact claim, checks whether the tests measure it, compares capability before and after mitigations, examines the evaluator’s independence and access, and records what remains unknown. The result should guide a concrete choice—training, evaluation, deployment, restricted access or delay—not produce a universal safe/unsafe label.
1. Turn a broad promise into a testable claim
Marketing language such as “our frontier model is safe” compresses many different assertions. Extract one statement that could be checked against evidence. OpenAI defines a safety claim as “A specific assertion about a model or system’s capabilities, behavior, or safeguards that bears on its safety and can be assessed against evidence.” The same principles say the claim should identify the risks and conditions it addresses, together with assumptions and limitations.
Write the claim with all of these fields:
- System: the model version, fine-tune, agent scaffold, tools and access controls.
- Capability or safeguard: what the system can do, or what control is supposed to prevent.
- Risk: the harm being considered, such as cyber misuse, privacy loss or unsafe biological assistance.
- Activity: training, internal evaluation, staged release or a named deployment.
- Conditions: users, prompts, tool permissions, monitoring, rate limits, geography and time period.
- Assumptions and exclusions: what the company did not test or assumes will remain true.
For example, “the deployed coding agent does not enable high-impact cyber abuse when authenticated users have restricted network tools, logging and human approval” is reviewable. It is narrower—and more useful—than “the model is harmless.” If a company will not state a model version, risk boundary or operating conditions, mark the claim as underspecified rather than filling in the gaps yourself.
2. Identify the decision the evidence is meant to support
A safety case is not just a pile of benchmark scores. OpenAI describes it as “A structured argument, supported by evidence, explaining why a model or system’s risks are adequately managed for a specified activity, such as training, evaluation, or deployment.” It should connect each claim to evidence and disclose assumptions, uncertainty and residual risk.
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Ask what decision would change if the evidence were negative:
- Training: Should a run proceed, pause or add controls?
- Evaluation: Is the test plan sufficient to characterize a dangerous capability?
- Deployment: Should release be staged, geographically limited, access-controlled or delayed?
- Operations: What monitoring, incident response and reassessment thresholds are required?
A framework that describes review meetings or approval gates is evidence that a process was declared. It does not establish that a safeguard worked on this model. Look for a decision record: the finding, the action taken, the owner, the deadline and the condition that would reopen the decision.
3. Test whether the evaluation matches the claim
Check the method, not just the score
NIST’s 2026 draft TEVV-Athlon Framework for Evaluating AI Systems states: “Test, evaluation, verification, and validation (TEVV) of artificial intelligence (AI) systems is used to provide evidence that systems can effectively meet individual or organizational goals while minimizing negative impacts.” Its four-stage method is intended to create customized assessments from an organization’s TEVV objectives. The document was an initial public draft; comments opened August 7, 2026, and closed October 6, 2026. It is not a final standard.
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- Write the harm or failure condition in observable terms.
- Choose tasks that can produce that condition, not merely a convenient proxy.
- Define pass, fail and inconclusive outcomes before looking at results.
- Record model version, evaluation date, system prompt, tools, scaffolding, sampling settings and human involvement.
- State which populations, languages, environments and attack styles were omitted.
A refusal benchmark may show that a model declines a set of prompts. It does not by itself show that an agent remains safe when it can browse, execute code, call an API, receive multi-turn pressure or encounter a malicious document. Conversely, a red-team failure in an unrestricted laboratory setup may not describe a deployment with narrower permissions. The test answers only the question its conditions support.
Look for realistic pressure and attempted circumvention
Ask whether the evaluator reproduced the system people will actually use. Relevant variables include tool permissions, system scaffolding, authentication, rate limits, monitoring, user skill, time pressure, multi-agent interaction and the ability to retry or distribute requests. Include direct misuse, indirect prompt injection, data poisoning, jailbreaks and benign users whose requests are ambiguous.
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OpenAI’s published third-party principles pose the plain question, “Do safeguards work under realistic conditions?” Treat that as a question to answer with test design, not as evidence that any particular safeguard works. A narrow benchmark can support a narrow conclusion; broad safety language requires correspondingly broad coverage or an explicit limitation.
4. Demand both pre-mitigation and post-mitigation results
Two evaluations answer different questions. A pre-mitigation test estimates the underlying capability or misuse potential. A post-mitigation test shows what changed after refusal training, filtering, monitoring, access controls or other safeguards. A March 2025 position paper argues that either view alone can mislead and recommends reporting both; attribute that recommendation to the paper’s authors rather than treating it as a universal rule.
| Evidence | Question it answers | What it cannot establish alone |
|---|---|---|
| Pre-mitigation capability test | Could the base model perform or assist with the risky task? | That deployment safeguards fail, or that users can access the capability. |
| Post-mitigation test | Did the stated controls reduce success under the tested conditions? | That the reduction persists after adaptation, tool changes or distribution shift. |
| Adversarial or adaptive testing | Can determined users circumvent the controls? | That every future attack path is covered. |
| Operational monitoring data | Are failures, abuse attempts and incidents detected in use? | That unobserved harms do not exist, especially with limited deployment. |
Require the company to show the measurement, not only a statement that mitigation occurred. Useful details include sample construction, attack budget, false-refusal rate, successful bypasses, severity thresholds, confidence intervals and known blind spots. If the model was changed between tests, treat the results as separate experiments and request a version-by-version explanation.
5. Examine independence, conflicts and assessor access
Who scoped and funded the work?
An outside logo does not automatically make an assessment independent. Record who selected the question, set the scope, paid the assessor, supplied data and approved publication. Ask for financial, employment and personal conflicts, plus the controls used to manage them. An assessor hired only to confirm a company-selected benchmark may provide useful testing while still being too narrow to validate the company’s larger claim.
OpenAI’s September 22, 2026 principles call for relevant expertise, independence mechanisms, security and confidentiality, defined scope and actionable findings. They are the company’s published principles, not an external audit of their implementation. Use them as criteria to inspect, not as proof that the criteria were met.
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Could the assessor see enough to verify the conclusion?
Ask what the evaluator could inspect: model checkpoints or controlled queries, system prompts, tool code, filtering rules, logs, incident records, training data documentation and deployment settings. Secure access can be appropriate when unrestricted disclosure would create security or intellectual-property risks, but confidentiality should not become a reason to hide the decisive evidence from all independent scrutiny.
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A January 2026 research paper proposes “AI Assurance Levels” and argues that some frontier claims require expert interpretation and deep, secure access to non-public information. This is a research proposal, not an established certification regime. A public summary may therefore be useful while remaining insufficient to verify every claim. Look for a description of access limits, tests the assessor could not run, and whether the company—not the assessor—controlled the final report.
6. Separate a company framework from proof that controls work
Company frameworks can reveal declared thresholds, responsibilities and escalation paths. They become stronger evidence when tied to model-specific results, mitigation plans and follow-up tests.
Frontier Safety Frameworks
Google DeepMind’s page lists Frontier Safety Framework version 3.1, dated April 17, 2026. It describes identifying capability levels, detecting them across the model lifecycle, planning mitigations and involving external parties where appropriate. The page says detailed evaluation results and mitigation plans appear in safety reports, with summaries in model cards. For a review, inspect those model-specific documents: a framework description alone does not demonstrate that a control reduced risk on a particular release.
Third-party assessment principles
OpenAI’s published principles emphasize scope, technical expertise, independence, security and actionable findings. Check whether a particular assessment documents each element, rather than assuming the principles describe every assessment the company has commissioned.
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Draft public methods and research proposals
NIST’s TEVV-Athlon draft offers a way to build customized evaluations; the 2025 position paper offers an argument for paired pre/post testing; the 2026 auditing paper proposes deeper assurance. These documents can improve your questions, but none is a binding certification or evidence that a named model passed.
7. Use capability trends without overgeneralizing them
Capability numbers are time- and test-specific. Attribute each result to the publisher, year, domain, model and benchmark. The UK AI Security Institute’s Frontier AI Trends Report says the length of cyber tasks models can complete unassisted—measured by the time a human expert would take—is doubling roughly every eight months. AISI also reports that in 2025 it tested the first model that successfully completed expert-level cyber tasks typically requiring more than ten years of human experience. The institute says it has evaluated frontier systems across domains critical to national security and public safety since November 2023.
Those findings describe AISI’s cyber testing and do not measure every model harm, every deployment or real-world risk in general. They are still relevant when a company claims that cyber safeguards are sufficient: rising tested capability increases the need for current, realistic and model-specific evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Check follow-through and residual risk
A persuasive report tells you what happened after a finding. Look for:
- A mitigation tied to a specific failure, with an owner and completion date.
- Re-testing after the change, using the same task plus adaptive variants.
- Access restrictions, staged rollout or a deployment delay when risk remains high.
- Monitoring and incident-response commitments that can detect recurrence.
- Residual risks, unresolved disagreements and explicit reassessment triggers.
“No critical issues found” is incomplete without the severity scale, coverage and handling of inconclusive cases. A report that lists vulnerabilities but omits whether they were fixed cannot support a deployment decision. A report that says safeguards were improved but supplies no before/after measurement supports only a process claim.
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9. Keep legal and voluntary evidence distinct
Do not treat a voluntary code, draft standard, company policy and binding law as interchangeable. The European Commission’s GPAI Code FAQ describes the Code as a voluntary compliance tool and says full compliance enforcement for GPAI obligations begins August 2, 2026, according to its search-result summary. Confirm the exact obligation, provider category and current legal text before drawing a compliance conclusion.
The AI Act Service Desk summary of Article 92 describes Commission powers to commission independent expert evaluations and request model access through APIs or other technical means, including source code. That page reflects a consolidated text dated July 27, 2026. Verify the current law and applicability for the model and jurisdiction in question; general guidance is not a finding that a provider complies.
10. A practical review workflow
- Copy the claim verbatim. Preserve qualifiers such as “under monitored access” and “for this model version.”
- Create a claim card. Fill in system, risk, activity, conditions, assumptions, exclusions and decision owner.
- Build an evidence map. For every claim, list tests, data, assessor, date, version, access level and limitations.
- Score fit, not reputation. Mark each item as directly measured, proxy evidence, process evidence, self-report, inaccessible or unknown.
- Compare before and after controls. Note capability, safeguard performance, bypasses and changed conditions.
- Inspect independence and security arrangements. Record funding, conflicts, scope authority and what could not be inspected.
- Trace consequences. Link findings to mitigations, release decisions, monitoring and reassessment triggers.
- Write the decision with uncertainty attached. State what the evidence supports, what it does not, and the control or test required before the next decision.
11. A compact comparison worksheet
| Axis | Questions to answer | Warning sign |
|---|---|---|
| Claim scope | Which model, capability, safeguard, risk, activity and conditions are covered? What is excluded? | Universal language with no version or boundary. |
| Test validity | Does the method measure the named risk? Are date, tools, conditions and limitations visible? | A score from an unrelated or opaque benchmark. |
| Mitigation evidence | Are pre- and post-control results available? What vulnerabilities remain? | “We mitigated it” without measurements. |
| Independence | Who scoped, conducted, funded and reviewed the work? How were conflicts addressed? | Outside branding with company-controlled scope and no conflict statement. |
| Access | Could assessors inspect relevant data, safeguards and deployment conditions securely? | Conclusion based only on a company-selected demonstration. |
| Follow-through | What action followed findings? What triggers reassessment? | No owner, deadline, retest or residual-risk statement. |
| Transparency and law | What is public, confidential, voluntary or legally required in this jurisdiction? | Calling a draft, code or policy a certification. |
These axes synthesize NIST assessment guidance, OpenAI’s published principles and the cited research proposals. They are a practical worksheet, not a universal score or certification.
12. Red flags that should change your decision
- The claim changes meaning when you ask for the model version, date or deployment conditions.
- Only post-mitigation refusals are shown, with no estimate of underlying capability.
- A benchmark is presented without task construction, tools, attack budget or failure examples.
- The assessor is called independent, but funding, conflicts, scope authority or access are undisclosed.
- Confidentiality prevents every independent reviewer from checking the decisive evidence.
- A framework, model card or voluntary code is described as proof that the system is safe in general.
- Known failures are listed without remediation, retesting or a release consequence.
- Capability figures are quoted without publisher, year, domain, model and test limitations.
13. How to write the final conclusion
Use a bounded conclusion with four sentences or four labeled fields:
- Supported: the exact claim the evidence substantiates.
- Not established: the broader or different claim the evidence cannot support.
- Unknown: inaccessible conditions, untested attacks, version changes or unresolved conflicts.
- Decision: deploy, restrict, test further, delay or proceed only with named controls.
For example: “The post-mitigation tests show fewer successful harmful-tool calls for model X under the documented access policy. They do not establish safety for unrestricted users, new tools or future versions. Adaptive testing and production monitoring were not available for review. Keep the staged rollout until those tests and an incident threshold are in place.” This form is more useful than assigning the company a permanent label.
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