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AI labs do not rely on one test or a universal “safe” score. They identify risks relevant to a model and its expected use, test for dangerous capabilities and harmful behavior, assess whether safeguards reduce the remaining risk, and decide whether to deploy, limit access, make changes, or keep evaluating. The process is repeated as models and deployment conditions change. A favorable evaluation supports a release decision; it does not prove that a model is harmless.
What does an AI safety evaluation actually decide?
An evaluation helps answer a practical question: given what the model can do, the protections around it, and the way it will be used, is the remaining risk acceptable for a proposed deployment? It is not a universal certification that a model is safe in every context. The answer can differ for the same model if one release is limited to a small group of users and another makes it broadly available with different tools or safeguards.
Labs first decide which risks deserve testing. OpenAI’s April 2025 Preparedness Framework, for example, prioritizes capabilities that could plausibly cause severe harm and considers whether a risk is measurable, severe, novel, and potentially immediate or difficult to reverse. Its framework uses “High” and “Critical” capability thresholds. Those labels belong to that framework; they are not industry-wide ratings.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPublic frameworks identify possible areas of concern such as cyber offense, chemical, biological, radiological, and nuclear (CBRN) risks, harmful manipulation, and loss of control. Which scenarios are relevant depends on the model’s capabilities, the tools it can access, and the deployment being considered. A broad chatbot, a model with external tools, and a system used in a sensitive organization may call for different evaluation priorities.
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What do AI safety evaluations test?
Testing can examine what a model is capable of doing, how it behaves when prompted in different ways, whether it can be controlled, and how the surrounding system might fail. No single benchmark covers all of these questions, so labs combine methods and interpret findings in context.
Capabilities and dangerous-use scenarios
Capability evaluations look for performance that could enable serious harm. Depending on the risk being assessed, a lab may use structured tasks, automated evaluations, or expert-led investigations. OpenAI says its Preparedness Framework combines scalable automated evaluations with expert-led “deep dives”; its capabilities reports assess whether a model has crossed a relevant risk threshold.
A test result is not automatically evidence that a model will cause harm. It indicates what the model can do under specified conditions, and helps the lab judge whether the capability could be put to harmful use. The evaluation therefore needs to consider the tested task, the model’s access to tools or information, and the assumptions behind the scenario.
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Controllability, broader harms, and system security
Capability is only part of the question. UK government guidance identifies dangerous capabilities, controllability, societal harms, and system security as areas for evaluation. A model may raise concerns because of what it can produce, how reliably it follows safeguards, or vulnerabilities in the wider system through which people access it.
That wider system matters: the model is not always deployed as a stand-alone text generator. The proposed use and configuration affect what risks the lab needs to assess. The available public guidance does not prescribe one test suite that answers all these questions for every model.
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Red teaming and expert review
Red teaming probes for failure modes that routine tests may not reveal. Evaluators deliberately explore ways a system might produce harmful outputs, respond to misuse, or fail under unusual conditions. UK guidance recommends red teaming and encourages access for independent external evaluators, particularly before deployment. External evaluation can add perspectives beyond the lab’s own teams, although a framework’s recommendation to invite such work does not establish that every release received it.
Automated tests can be repeated at scale, while expert-led work can investigate subtler or less anticipated risks. OpenAI describes using both, with experts checking that scalable evaluations measure the intended concerns. Neither method makes the other unnecessary: a large test set can miss an important failure mode, and a focused expert investigation cannot by itself test every possible interaction.
When do labs test a model?
Safety evaluation is a lifecycle process rather than a final exam conducted once, just before launch. UK government guidance describes assessment before training, checkpoint testing during training and fine-tuning, extensive pre-deployment evaluation, and post-deployment reassessment when appropriate.
Before and during training
Before training a new model, a lab can evaluate a predecessor or analogous model to estimate how relevant properties might scale. That provides an early opportunity to identify risks and prepare evaluations, but it is not a substitute for testing the model that will actually be released. During training and fine-tuning, checkpoint testing can reveal changes in relevant capabilities or behavior before the work is complete.
Evaluation at these stages can inform whether development should continue as planned, whether additional safeguards are needed, or whether a threshold requires a different response. UK guidance advises labs to consider what actions will follow if their pre-specified thresholds are reached, including pausing development or deployment if agreed mitigations are not in place.
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Before deployment
Pre-deployment evaluation examines the model and intended release in more depth. The lab can assess relevant capabilities, red-team the system, review security, and consider whether safeguards are adequate for the residual risk. UK guidance recommends extensive evaluation before deployment and independent external evaluation where possible.
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Results should be interpreted against the proposed release, not treated as an abstract property of a model. A decision about one deployment may not carry over to a different version, access level, tool configuration, or user group.
After release
Post-deployment evaluation checks whether previous conclusions still hold as the system is used and changes. UK guidance recommends reassessment at intervals and after notable changes that could make earlier evaluations obsolete. It describes possible responses such as updating safeguards, strengthening security, temporarily limiting access, or rolling back a deployment.
How do evaluation findings affect a release decision?
A test result feeds a risk decision; it does not dictate one universal outcome. Labs compare findings with their own thresholds or decision criteria, examine mitigations, and judge the residual risk for the deployment being considered. Depending on that assessment, a lab may release with safeguards, restrict access, delay deployment, modify the system, or continue evaluating.
OpenAI’s April 2025 Preparedness Framework says systems reaching its High capability threshold must have safeguards that sufficiently minimize the associated severe risk before deployment. At the Critical threshold, it also requires safeguards during development. The framework describes a Safety Advisory Group reviewing whether safeguards sufficiently minimize severe risk and making recommendations to leadership. The group may recommend approval, further evaluation, or stronger protections; OpenAI Leadership makes the final deployment decision.
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UK government guidance recommends setting risk thresholds in advance and linking each threshold to additional mitigations. That approach makes it clearer what a lab intends to do when a result is concerning, rather than allowing the response to be improvised after a threshold is reached. The remaining risk still has to be assessed: a safeguard can reduce risk without eliminating it.
How do public AI safety frameworks differ?
Public frameworks can be compared by their stated risk scope, evaluation methods, thresholds, safeguards, decision authority, lifecycle coverage, and transparency. Their labels should not be treated as interchangeable: one organization’s threshold or review structure does not establish another organization’s release standard.
| Framework or guidance | What it says publicly | What it does not establish |
|---|---|---|
| OpenAI Preparedness Framework (April 15, 2025) | Tracks severe-harm capabilities; describes High and Critical thresholds, automated evaluation, expert-led deep dives, safeguards, Safety Advisory Group recommendations, and leadership decisions. | It is OpenAI’s stated framework, not a universal industry scale or independent proof that every test is effective. |
| OpenAI Frontier Governance Framework (May 28, 2026) | Addresses cyber offense, CBRN risks, harmful manipulation, and loss of control, alongside model reporting, security risk management, incident response, and external expert input. | The public description does not make its categories equivalent to another lab’s thresholds. |
| UK government, “Emerging processes for frontier AI safety” | Recommends evaluation before training, at checkpoints, before deployment, and after release; includes dangerous capabilities, controllability, societal harms, security, red teaming, external evaluation, thresholds, and mitigation planning. | It is guidance about processes, not a certification for a particular model. |
| NIST AI Risk Management Framework (AI RMF) | A voluntary, broad framework for managing risks to individuals, organizations, and society across AI design, development, use, and evaluation. NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile on July 26, 2024. | It is not a release test or certification for a particular model. NIST says the framework is being revised. |
| Google DeepMind Responsibility & Safety | Describes review by a Responsibility and Safety Council against its AI Principles, and an AGI Safety Council focused on extreme risks. | The public page does not disclose a complete model-specific release testing protocol. |
NIST’s AI Resource Center provides materials supporting testing, evaluation, verification, and validation under the AI RMF. Those resources support risk-management work; they do not certify that a particular model is safe to release. The distinctions in this table reflect what the named public materials describe, not an independent audit of how consistently organizations apply them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why can a model pass evaluations and still pose risks?
Evaluation conclusions are bounded by what was tested and the assumptions used. A test can miss behavior outside its scenarios, while new capabilities, fine-tuning, tools, or deployment changes can create risks that earlier evaluations did not cover. Test results also depend on access to the model, prompting and tooling, threat assumptions, and the proposed use.
For that reason, “passed safety” is too broad unless it is tied to a defined evaluation and context. A more informative account would specify the risks assessed, methods used, findings, safeguards, and limits of the evaluation. Public frameworks describe intended processes, but their existence alone is not independent evidence that every test was effective or every release followed the process exactly.
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In a March 21, 2024 TIME article, METR policy director Chris Painter said, “The evals are not ready.” The article also quoted NIST official Elham Tabassi saying, “I’m not going to pretend to say that we-NIST-have all of the answers.” Those comments are a dated warning about the state of evaluation at that time, not a description of every current lab’s methods. The broader point remains that an evaluation is evidence for a decision under uncertainty, not proof of harmlessness.
What should readers look for in a lab’s public safety report?
A useful report lets readers understand what was assessed and how the result affected deployment. The detail available varies among organizations, so compare the information actually published rather than assuming that similar labels mean similar evidence.
- Risk scope: Which hazards and societal effects were considered, and why were they relevant to this model?
- Evaluation methods: Did the lab describe automated tests, expert-led review, red teaming, external evaluation, or a combination?
- Thresholds and consequences: Were criteria specified, and does the framework say what action follows when a threshold is reached?
- Safeguards and residual risk: What protections are described, and does the report explain what risk remains?
- Release authority: Who reviews evaluation findings, and who makes the deployment decision?
- Lifecycle coverage: Does the stated process include training checkpoints and reassessment after deployment or changes?
- Transparency: What findings and rationale are public, and what information is not disclosed?
These questions help distinguish a substantive description of a decision process from a general assurance. They do not by themselves prove that a lab’s evaluations are complete; they show what readers can and cannot verify from the public account.
Frequently Asked Questions
What is the difference between AI safety testing and AI certification?
Safety testing produces evidence about particular risks under particular conditions. The NIST AI Risk Management Framework is voluntary risk-management guidance, and the cited materials do not describe it as a certification for a specific model.
Can an outside evaluator test an AI model before it is released?
UK government guidance recommends access for independent external evaluators, especially before deployment. Whether an evaluator can test a system depends on the access the lab provides; a recommendation in public guidance is not proof that an external review occurred for a given release.
Does a public safety framework prove a lab followed it for a particular model?
No. A public framework describes a stated process, while evidence about a particular release would need to show what was evaluated and how the findings informed the decision. The public descriptions cited here do not independently verify that every release followed the published process.
What is NIST’s Generative AI Profile for?
It is a profile associated with NIST’s broader AI Risk Management Framework, released July 26, 2024. It supports risk-management work; it is not a standalone pass/fail release certificate for a model.
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