An AI safety case should set out a defined safety claim for a particular system, deployment and environment, then show through clear reasoning and relevant evidence why that claim is justified. It is not simply a collection of test results or a general label that a model is “safe.”
What is an AI safety case?
The AI Security Institute quotes Defence Standard 00-56’s definition of a safety case as “A structured argument, supported by a body of evidence, that provides a compelling, comprehensible, and valid case that a system is safe for a given application in a given environment.” Read the AISI explanation.
That definition makes the boundary essential: a case supports a specified use under stated conditions, not every possible use of a model. It should let a reviewer inspect what is being claimed, why the reasoning supports it, and whether the evidence is adequate for the decision at hand.
Start with the system, use and decision
Identify the system being assessed and the decision the case is meant to support. Describe the model or system version and configuration, intended users, purpose, operating environment and deployment boundary. State what is outside scope.
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For example, a claim about an AI assistant used by trained staff in a supervised workflow does not automatically cover public access, different tools, changed permissions or unsupervised use. Define those conditions rather than implying the conclusion travels with the model wherever it goes.
Make the top-level safety claim precise
Say what “acceptably safe” means for this application and which safety objective the case addresses. Connect the objective to the relevant hazards and the people, assets or services that could be affected. Also state what evidence and acceptance criteria are considered sufficient for the deployment decision.
A broad statement such as “the model is safe” is not reviewable on its own. The AISI emphasizes that a case needs a precise account of what safe means, evidence for the claim, and an argument connecting the evidence to that claim. See the AISI discussion of safety cases for frontier AI.
Map hazards, harm pathways and assumptions
Describe plausible ways harm could occur, including foreseeable misuse and operation outside the intended environment. Identify relevant threat actors, vectors and targets where applicable, as well as assumptions about users, access, tools and safeguards. For a cyber risk, for instance, the argument can make explicit who might act, how a harmful outcome could be caused, and what or whom it could affect.
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Assumptions belong in the case because they can determine whether its conclusion holds. If safety depends on human review, restricted access or a particular operating context, say so; do not leave those conditions implicit.
Build the argument from claims, reasoning and evidence
Keep three elements distinct: claims say what must be true; arguments explain why the evidence supports those claims; evidence provides the observations or analysis on which the reasoning relies. The Information Commissioner’s Office describes assurance cases in these terms and notes that subordinate claims and assumptions can support an argument. See the ICO guidance on assurance cases.
Break the top-level claim into assessable subclaims. For each one, explain which evaluation, mitigation, process or operational control supports it, and how the subclaims together support the overall conclusion. Make the rationale and inferential steps visible. A test result is not self-explanatory: the case must show why that test is relevant to the claim and what it does—and does not—establish.
Choose evidence that matches each claim
Evidence should be relevant to the specific subclaim and detailed enough for a reviewer to assess its strength. Depending on the question, a case may use empirical, conceptual or mathematical arguments, along with evidence about deployment conditions and organisational factors. AISI also discusses negative evidence, such as a well-incentivised red team failing to defeat safety methods; such a result can inform an argument but is not, by itself, proof of safety.
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Preserve the information needed to interpret and challenge findings:
- Method and scope: what was evaluated, how, and which system configuration or conditions were covered.
- Test conditions: relevant datasets, scenarios, tools, participants and assumptions.
- Results and limitations: what the evaluation found, what it did not test, and known weaknesses in the method.
- Provenance and interpretation: where the evidence came from, how it was recorded, and why it supports the claim.
The ICO says an assurance case’s evidence base should consist of objective, demonstrable and repeatable information recorded during production and use. Retaining methods, conditions and limitations helps others reproduce or challenge the interpretation.
Explain mitigations and how the system is operated
Describe the safeguards and operational controls that contribute to safety. For each material control, identify who owns it, the conditions under which it works, and what happens if it fails or the system crosses its intended boundary. Include relevant monitoring, escalation and response arrangements.
The Defence Science and Technology Laboratory’s assurance handbook says assurance should consider how to detect use outside the intended environment and respond to maintain safety. Read the Dstl handbook. A control that exists only on paper, without an owner or a failure response, is difficult to rely on in the argument.
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Include people and organisational conditions
Where they affect the safety claim, address who is responsible for decisions and controls, whether staff have suitable competence and training, how concerns are escalated, and what organisational practices shape deployment. AI safety is sociotechnical: the model’s behavior is only part of the operating conditions a case may need to support.
The AISI cautions that its proof of concept for an inability argument is not a complete safety case; a full case for a current system would also include sociotechnical arguments. The same source says that writing cases for substantially more advanced systems remains an open research problem.
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Do not present the argument as stronger than its evidence. Record limitations, conflicting findings, residual risks, open assumptions and plausible ways the reasoning could fail. Include efforts to find evidence that might undermine the case, not only results that support it; Dstl’s handbook calls for both.
State what changes would invalidate or require reassessment of the case. A material change to the model, tools, data, users or deployment conditions can alter the hazards, controls or evidence on which the conclusion depends. Revisit the argument when those conditions change.
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Reviewing or comparing AI safety cases
There is no universal official scoring rubric in the cited guidance. As a practical review, compare cases across these dimensions:
- Deployment and decision scope: Is the system, use, environment and boundary clear?
- Hazards and affected parties: Are plausible harm pathways, targets and foreseeable misuse covered?
- Evidence quality and relevance: Does the evidence address the claims, and can its methods and limits be assessed or reproduced?
- Reasoning and uncertainty: Are assumptions, inferential steps, limitations and counterevidence visible?
- Operational assurance: Are monitoring, ownership, out-of-scope detection and response defined?
Use this structure alongside applicable requirements
This is a practical structure, not a checklist established for every AI system, sector or jurisdiction. Check the legal and sector-specific obligations that apply to the system as well. The UK government’s introduction to AI assurance points readers to broader governance and risk-management resources, including NIST’s AI Risk Management Framework. NIST notes that human intervention may be needed when a system cannot detect or correct errors, and that safety risk management may require approaches tailored to context and severity; these resources complement rather than replace an explicit safety argument and evidence chain. UK government introduction to AI assurance and NIST AI Risk Management Framework.
The ICO’s AI and data protection guidance page says it is under review following changes made by the Data (Use and Access) Act. Check the current guidance and applicable requirements when preparing a case. ICO AI and data protection guidance.
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