A safety case is a structured, evidence-backed argument that a system is safe enough — long standard in nuclear/aviation/AV regulation, now proposed as the assurance backbone for frontier AI

Buhl, Sett, Koessler, Schuett & Anderljung (Centre for the Governance of AI, Safety cases for frontier AI, arXiv:2410.21572) define a safety case as "a structured argument, supported by evidence, that a system is safe enough in a given operational context." The framework has four components: objectives (what "safe enough" means here), arguments (the structured reasoning), evidence (what grounds the reasoning), and scope (the boundary conditions under which the case holds).

Safety cases are not new: they are the standard assurance instrument in nuclear power, civil aviation, and autonomous-vehicle regulation, where a regulator or operator must certify a system before deployment rather than after an incident. The paper's contribution is applying the same instrument to frontier AI — proposing that labs write structured, falsifiable arguments for why a given model is safe enough to deploy, rather than relying on informal risk assessments. The paper describes Anthropic as already folding "affirmative cases" into its Responsible Scaling Policy sketch for ASL-4, an early sign of the framework migrating from regulated physical infrastructure into AI-lab governance practice.

The argument shape a safety case takes — typically rendered as Goal Structuring Notation (GSN) — has its own lineage; see claim-toulmin-1958-argument-model-underlies-gsn-safety-cases. And the apparatus has a documented failure mode when the argument is built to confirm rather than to test: see claim-nimrod-safety-case-was-tick-box-compliance-exercise.