A safety case is a structured rationale arguing that a system is unlikely to cause harm, a practice long used in safety-critical industries such as aviation and nuclear power. Applied to AI, a safety case is a documented argument that a particular AI system is unlikely to cause a catastrophe, assembled from concrete evidence rather than informal assurance. The approach was set out for frontier AI in the 2024 paper “Safety Cases: How to Justify the Safety of Advanced AI Systems” by Joshua Clymer, Nick Gabrieli, David Krueger, and Thomas Larsen.
The paper proposes four broad categories of argument a developer might make. Total inability: the system simply cannot cause the harm in question. Control measures: safeguards are strong enough to prevent the harm even if the system tried. Trustworthiness: the system is reliable enough not to attempt the harm despite having the capability. And deference to AI advisors: for very powerful systems, relying on credible AI oversight. A real safety case typically combines several of these into a coherent whole.
The value of the framing is that it forces developers to state, explicitly and in advance, why deployment is safe, and it gives regulators a shared structure for scrutinizing that claim.
For a business reader, the safety case is the bridge between AI and the rest of high-stakes engineering: it reframes safety from a vague aspiration into an auditable argument that can be challenged, strengthened, or rejected.