A safety checklist is useful, but it rarely convinces stakeholders on its own. A safety case is stronger: it makes explicit claims about a system (“it will not do X”), explains why those claims are credible, and references evidence that can be reviewed and updated over time.
This approach is common in safety-critical industries. It maps well to enterprise AI because AI systems change frequently—models update, knowledge bases evolve, prompts change, and tools expand.
Start with clear, testable claims
Good claims are specific. Examples:
- The assistant does not disclose sensitive data across users or tenants.
- High-risk tool actions require explicit human approval.
- Answers about policy are grounded in approved sources and include citations.
These claims connect directly to architecture controls (see guardrails and policy layering).
Build an argument: why the claims should hold
A safety case includes an argument that ties claims to controls. For example:
- Permission enforcement happens in retrieval filters, not in prompts.
- Tool use is authorised by a deterministic policy layer (see tool authorisation).
- Prompts and policies are versioned with change control (see prompt change control).
Attach evidence (and keep it current)
Evidence can include:
- Evaluation results. Benchmarks, red teaming, and regression tests (see red teaming).
- Architecture artifacts. Threat models, data flow diagrams, boundary definitions.
- Operational data. Monitoring dashboards, incident logs, SLO performance (see AI SLOs).
The “living” part matters: evidence should be updated when the system changes. This is where drift monitoring and release discipline are essential (see drift monitoring and canary rollouts).
Use the safety case to drive decisions
A safety case is not a document for its own sake. It becomes a decision tool:
- If a new tool is added, which claims are affected?
- Which new evidence is required before promotion to production?
- What monitoring and incident playbooks must exist first?
When used well, a safety case helps teams ship faster—because the organisation can see the controls and evidence that make that speed safe.