SafeMachine — Governance Framework for AI and Automation

SafeMachine — Governance Framework for AI and Automation

SafeMachine is the framework Cinderpoint Applied uses when an organisation wants to deploy AI or automation without losing sight of who is responsible for what. It is not a technical standard and not a checklist. It is a way of designing structures so that AI systems remain accountable, observable, and interruptible over time.

The three components of SafeMachine

  • BASE. A structural map of how decisions are currently made, which systems are involved, who can override them, and where drift is likely to occur as AI is introduced.
  • SAFEARC. A lifecycle governance arc for AI systems, from scoping and design through deployment, monitoring, and retirement. It defines who signs off on what, and which obligations attach to different stages of the system’s life.
  • STABLE. A crisis and escalation cycle for when something has already gone wrong — a harmful decision, a major bug, a regulatory incident, or a public failure of trust.

How SafeMachine is applied in practice

  1. Map the current structure (BASE). We document the real decision paths, not the org chart version. This includes model owners, process owners, vendors, and any silent dependencies.
  2. Shape the lifecycle (SAFEARC). We design or refine the AI system’s lifecycle so that responsibilities, approvals, logs, and tests are attached to specific stages rather than left vague.
  3. Install escalation (STABLE). We define what happens when things break: who can stop a system, how incidents are investigated, and how lessons are folded back into design and governance.

Where SafeMachine is most useful

  • When AI systems are moving from experimentation into core operations.
  • When automation is applied to decisions that affect people’s rights, livelihoods, or safety.
  • When boards or executives need to be able to explain their governance posture to regulators, partners, or the public.

SafeMachine is designed to be used with imperfect information and under time pressure. It does not make AI safe by assertion. It makes the structure of governance clear enough that safety, accountability, and escalation can be designed and defended in public.