AI Safety Case

An AI safety case is a structured argument that an AI system can be operated safely within a clearly defined use case. It brings together assumptions, risks, controls, tests, evidence, and operating limits so technical, business, and compliance teams can inspect the same safety logic. The key word is argument: the document explains which harms matter, which controls reduce them, and what evidence shows those controls work. For agentic systems, a safety case may cover permission boundaries, shutdown paths, audit logging, red teaming, human approvals, monitoring, and escalation procedures. It also has a scope. A safety case is not a blanket claim that “the model is safe”; it applies to a specific application, model version, data context, tool access, and production environment. When any of those elements change, the case needs to be reviewed. The concept matters because AI safety is moving from vague assurance to inspectable proof. Organizations need to show not just that they tested a system, but why that system is acceptable under specific conditions.