Do not begin by asking whether an AI company is “safe.” Begin by asking what, exactly, it claims, what evidence could support that claim, and what decision the evidence is meant to inform. A credible assessment identifies a particular model or system, a defined risk, operating conditions, assumptions, limitations, and a consequence if the evidence is weak.
This guide turns that approach into a practical review method for model cards, safety reports, third-party assessments, benchmark results, and regulatory disclosures.
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What is an AI safety claim?
OpenAI’s September 22, 2026 document, Priorities and principles for effective third party assessments, 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 important words are specific and assessed against evidence.
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Replace a broad promise with a testable statement
Rewrite the company’s language into a sentence that could be proved or disproved. For example:
- “For model version X, with the documented tools and access controls, the deployment blocks the specified class of requests at the reported rate under the tested conditions.”
- “Before deployment, model X did not exceed the stated capability threshold on the named evaluations, and after mitigation the residual failure modes were within the company’s release criteria.”
Those statements still need evidence, but they tell you what to look for. Record what is excluded: other model versions, different tools, unrestricted users, longer conversations, fine-tuned copies, or attacks not represented in the test.
Understand the safety case
OpenAI uses “safety case” for “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.” A safety case connects individual claims to tests, controls and decisions. It should also state assumptions, uncertainties and remaining risks that could change the conclusion.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsA framework describing a process is evidence that the company has declared a process. It is not, by itself, evidence that a safeguard worked on a particular model. Look for model-specific results and a record of what happened when testing found a problem.
Step 1: Pin down the exact claim, model and decision
Before judging a chart or a headline, write down the scope. Use this checklist for every claim:
- System: Which model, checkpoint, product configuration, agent, or service is covered? Record the version, release channel and evaluation date.
- Risk: Is the claim about cyber misuse, biological assistance, privacy, deception, autonomy, reliability, abuse prevention, or another hazard?
- Activity: Does it support training, an internal evaluation, a limited pilot, or public deployment? The acceptable evidence can differ by activity.
- Conditions: What prompts, tools, system scaffolding, network access, rate limits, user permissions and monitoring were present?
- Outcome: Is the company claiming a capability threshold was not crossed, that a safeguard reduced harmful behavior, or that residual risk is acceptable?
- Assumptions and exclusions: What users, attacks, languages, integrations, model updates and failure modes were not covered?
- Decision: What action depends on the result: release, wider access, a training change, a delay, or additional testing?
If a document does not answer these questions, classify the claim as underspecified rather than filling the gaps with optimistic assumptions.
Step 2: Check whether the test measures the stated risk
A score is meaningful only in relation to the question and the method that produced it. Ask whether the evaluation task resembles the harm described in the claim and whether the result is relevant to the actual deployment.
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Use a test-fit audit
- Construct fit: Does the task measure the capability or behavior named in the claim, or merely a convenient proxy?
- Coverage: Are ordinary use, expert misuse, adversarial prompting, multi-turn interaction and tool use represented where relevant?
- Configuration: Is the evaluated model the one users receive, with the same system instructions, retrieval, tools and access controls?
- Timing: Are the model version and evaluation date visible? A result from an earlier checkpoint may not describe a later system.
- Baselines: Can you compare performance before a mitigation, with the mitigation enabled, and against a meaningful threshold?
- Limitations: Does the report state sampling choices, evaluator disagreement, false positives, false negatives and untested cases?
What the NIST TEVV-Athlon draft contributes
NIST’s 2026 TEVV-Athlon Framework for Evaluating AI Systems describes a four-stage method for creating customized assessments from an organization’s test, evaluation, verification and validation objectives. Its central idea is that assessments should be designed around the decision and risk, rather than selecting a generic benchmark first. NIST 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.”
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The page describes the document as an initial public draft. The comment period opened August 7, 2026, and closed October 6, 2026. Treat it as draft guidance, not a final certification or mandatory rule.
- Set the objective: Define the organizational goal, risk tolerance and decision the assessment must inform.
- Design the assessment: Select methods, data, environments, metrics and acceptance criteria that match that objective.
- Run and verify: Execute tests, check the integrity of measurements, and document deviations from the planned conditions.
- Validate and use: Determine whether the evidence supports the intended decision, communicate uncertainty, and revise the assessment when the system or risk changes.
Step 3: Demand both pre-mitigation and post-mitigation evidence
Two different questions are often collapsed into one release chart:
- Pre-mitigation testing: What could the underlying model do before refusal training, filters, monitoring, access controls or other safeguards?
- Post-mitigation testing: How did those controls change behavior under the conditions that matter, and what bypasses remain?
A March 2025 position paper argues that either view alone can mislead. A low post-mitigation score may conceal a powerful underlying capability, while a high pre-mitigation score does not show whether safeguards successfully reduced practical misuse. The authors also identify inconsistent methods and vague disclosures as weaknesses in the material they reviewed. Attribute this recommendation to that paper rather than treating it as a universal legal requirement.
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What to request from a report
| Evidence item | Question it answers |
|---|---|
| Baseline capability results | What could the model do before the relevant controls? |
| Mitigation description | Which training change, filter, monitor, permission or product control was applied? |
| Matched post-mitigation tests | Did the same tasks, with comparable sampling, show a change? |
| Adversarial and circumvention tests | Can users regain the behavior through prompt variation, multi-turn interaction or tool pathways? |
| Residual-risk analysis | Which failures remain, how severe are they, and what triggers another review? |
Do not compare a pre-mitigation capability score with a post-mitigation refusal rate as if they were the same metric. They answer different questions and require the test design to explain how they relate.
Step 4: Test safeguards under realistic conditions
OpenAI’s published assessment principles pose the direct question: “Do safeguards work under realistic conditions?” Use it as a prompt to examine the gap between a laboratory setup and the system people can actually use.
Check the operational environment
- Tools and permissions: Was the model connected to code execution, browsers, files, databases, messaging or other tools available in deployment?
- System scaffolding: Were agent loops, memory, retrieval, orchestration and automatic retries enabled?
- Users and incentives: Did evaluators include ordinary users, skilled operators, malicious users or organizations with different access levels?
- Persistence: Were long conversations, repeated attempts, account creation and rate-limit workarounds tested?
- Monitoring: Were detection, human review, incident response and logging part of the safeguard, and were they tested for evasion?
- Distribution shift: Does the evaluation cover languages, domains, data formats and integrations that differ from the benchmark?
A narrow benchmark can be valid for a narrow claim. It becomes misleading when a company presents it as evidence for broad safety in an open-ended product.
Separate capability evidence from harm evidence
A model’s ability to complete a task is not the same as proof that real-world harm occurred, and a refusal benchmark is not a complete measure of misuse risk. Record whether a result is a capability demonstration, a policy-compliance test, an attack simulation, an incident analysis or an operational control test. Each supports a different part of the safety case.
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“Third party” does not automatically mean independent. Investigate who chose the question, wrote the protocol, paid the assessor, supplied the data, reviewed the draft and controlled publication.
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Questions for an external assessment
- What technical and domain expertise did the assessors have for the risk being tested?
- Could the company veto methods, findings or publication, and are those limits disclosed?
- Who funded the work, and were financial, employment or personal conflicts addressed?
- Did the assessor choose the sample and attack methods, or only execute a company-designed script?
- Was there a second reviewer, red-team challenge or reproducibility check?
- Are disagreements, failed tests and omitted analyses visible?
OpenAI’s September 22, 2026 principles call for defined scope, technical expertise, independence mechanisms, security and confidentiality, and actionable findings. Those are OpenAI’s stated principles, not independent proof that every assessment follows them.
A January 2026 research paper proposes frontier AI auditing as deep, secure third-party verification against relevant standards. Its “AI Assurance Levels” framing is a research proposal, not an established certification regime. Use it to ask whether an audit had enough depth and authority, not to assign a formal level to a company.
Step 6: Ask whether the assessor could actually verify the claim
Public transparency has limits. Some evidence may contain sensitive weights, personal data, security controls or information that would enable misuse. But confidentiality is not a reason to accept an unsupported conclusion.
Assess access in layers
| Access layer | What an assessor can evaluate | Typical blind spot |
|---|---|---|
| Public report only | Declared scope, methods, headline results and limitations | Unpublished failures, raw data, model configuration and disputed findings |
| Secure test environment | Reproducible behavior with controlled prompts, tools and monitoring | Production changes, hidden operational dependencies and longer-term incidents |
| Confidential technical access | Logs, model versions, safety tooling, evaluation data and deployment controls | Evidence outside the agreed scope or information withheld for legal or security reasons |
| Regulatory or legally compelled access | Broader inspection powers, subject to the applicable law and safeguards | Jurisdiction, timing, secrecy and the precise authority granted |
The January 2026 auditing proposal argues that some claims require expert interpretation and deep, secure access to non-public information. State clearly what the assessor saw, what was redacted, and which conclusions depend on company-supplied descriptions.
Step 7: Look for consequences and follow-through
An evaluation matters when it changes a decision or control. Look for a documented link between findings and action:
- Was a release delayed, narrowed or cancelled?
- Were access tiers, rate limits, monitoring or user verification changed?
- Was additional training or adversarial testing commissioned?
- Does the report identify residual risk and a person or team accountable for it?
- Are there reassessment triggers, such as a new capability level, tool integration, incident or model update?
- Have later reports shown whether the mitigation remained effective?
A policy that says the company will reassess after a threshold is evidence of declared governance. It becomes stronger evidence when the company publishes an example of applying that policy, including an adverse finding and the resulting restriction or remediation.
How to compare two companies or reports consistently
Use the same questions for every provider. The following table is a review worksheet, not a universal score or certification.
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| Axis | What to ask | Strong evidence looks like |
|---|---|---|
| Claim scope | Which model, capability, safeguard, risk, activity and operating conditions are covered? What is out of scope? | A versioned claim with assumptions, limits and a stated release decision |
| Test validity | Does the method measure the stated risk? Are date, model version, tools, conditions and limitations visible? | Customized tasks, justified metrics, transparent sampling and known uncertainty |
| Mitigation evidence | Are results available before and after safeguards? What changed and what remains vulnerable? | Matched tests, bypass attempts, residual-risk analysis and update history |
| Independence | Who scoped, conducted, funded and reviewed the assessment? How were conflicts addressed? | Independent method control, disclosed funding and documented challenge of company assumptions |
| Access | Could assessors inspect enough data, safeguards and deployment conditions to support the conclusion? | Secure access with a clear inventory of inspected and withheld material |
| Follow-through | What action followed findings? Are unresolved risks and reassessment triggers reported? | Release restrictions, mitigations, owners, deadlines and later verification |
| Transparency and legal context | What is public, confidential, voluntary or legally required for this model and jurisdiction? | Precise legal basis and disclosure boundaries, without presenting voluntary guidance as law |
How to read company safety frameworks and model cards
Frameworks describe intent; reports show application
Google DeepMind’s Frontier Safety Framework version 3.1 is dated April 17, 2026. Its page describes identifying capability levels, detecting them across the model lifecycle, planning mitigations and involving external parties where appropriate. It says detailed evaluation results and mitigation plans appear in safety reports, with summaries in model cards.
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When reviewing that type of framework, ask whether the specific model’s report shows the threshold tests, mitigation decision, external involvement and residual risk. A framework without those model-level records cannot establish that a safeguard worked in this release.
Company-authored assessment principles need corroboration
OpenAI’s third-party principles are useful for checking whether an assessment has scope, expertise, independence protections, security and actionable findings. Because the principles are authored by OpenAI, corroborate implementation through the assessor’s report, disclosed access, raw or summarized results, and evidence of decisions that followed.
Public capability trends provide context, not a safety verdict
The UK AI Security Institute’s Frontier AI Trends Report describes two years of public, evidence-based testing. AISI says it has evaluated frontier AI systems across domains critical to national security and public safety since November 2023.
In its cyber domain, AISI reports that the length of cyber tasks models can complete unassisted, expressed as the time a human expert would take, is doubling roughly every eight months. AISI also says it tested the first model in 2025 that could successfully complete expert-level cyber tasks typically requiring more than ten years of human practitioner experience.
Those figures are specific to AISI’s tests, dates and cyber-task definition. They are not a measure of every AI capability, every harm or every deployment. Use them to justify fresh testing when capability changes, not to infer that a model is generally unsafe or safe.
How legal and voluntary schemes fit into your review
Do not treat a voluntary code, draft framework, company commitment and binding law as interchangeable.
European Union GPAI materials
The European Commission’s GPAI Code FAQ describes the Code as a voluntary compliance tool. Its search summary says full compliance enforcement for GPAI obligations begins August 2, 2026. The exact obligation, provider applicability and enforcement route must be checked against the current official legal text before making a compliance conclusion.
Commission evaluation powers
An 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 says it reflects a consolidated text as of July 27, 2026. Applicability, procedural safeguards and the law currently in force should be verified for the specific provider and model.
Regulatory access can make verification more authoritative, but it does not turn every public benchmark or company statement into proof of safety. It changes who may inspect evidence and under what legal authority.
A practical evidence worksheet
For each claim, create a one-page record. This prevents a persuasive headline from becoming an unsupported conclusion.
- Claim text: Copy the precise statement and identify who made it.
- System and date: Record model version, product configuration, evaluation date and deployment region.
- Risk and decision: State the hazard and whether the evidence supports training, evaluation, restricted access or deployment.
- Methods: List tasks, datasets, prompts, tools, evaluators, sample size, metrics and acceptance criteria.
- Before/after: Separate baseline capability results from post-mitigation behavior.
- Realism: Note whether production-like users, scaffolding, monitoring, persistence and circumvention were tested.
- Independence and access: Record funder, conflicts, assessor authority, secure-access arrangements and withheld material.
- Findings and action: Link each important failure to a mitigation, delay, restriction, escalation or decision not to act.
- Unknowns: List untested conditions, disputed interpretations, unavailable logs and assumptions that could change the result.
- Reassessment trigger: Specify what model, tool, incident or capability change requires the claim to be revisited.
Common ways safety evaluations go wrong
Using one score as a universal guarantee
A benchmark score is time- and test-specific. Attribute it to the named publisher, year, domain, model and method. Do not convert it into a claim about all harms or future versions.
Calling an outside review independent without examining control
An external logo on a report does not reveal who selected the question, supplied the data or limited publication. Independence is a set of protections that must be described.
Reporting only the mitigated result
Without a baseline, readers cannot tell whether the safeguard addressed a weak capability, a serious capability or merely the tested prompt style.
Confusing process with effectiveness
A governance chart, risk register or future commitment shows intent. Effectiveness requires results on the relevant system and evidence that findings changed controls or decisions.
Ignoring deployment changes
Adding a browser, code interpreter, memory, new data source or broader user access can alter risk even when the underlying weights are unchanged. Reassess the claim when the operating conditions change.
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After completing the review, classify the claim rather than the company. A claim is relatively well supported when its scope is specific, the test matches the risk, both capability and mitigation evidence are available, realistic conditions were examined, independent expertise and secure access are credible, and findings led to proportionate action.
If one of those links is missing, narrow your conclusion to what the evidence actually establishes. You might be able to say that a model passed a named evaluation under stated conditions, that a safeguard reduced a measured behavior in a test environment, or that a company has published a process for reassessment. You cannot honestly extend that result to every user, tool, future model or form of harm without additional evidence.
The most useful question is therefore not “Is this company safe?” It is: “For this model, risk, activity and operating conditions, does the available evidence justify the decision being made, and what remains unknown?”
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