Verifiable Intelligence: a Technical Framework for Auditable AI Governance

The race in artificial intelligence is no longer defined by scale or accuracy, but by the transparency of decision-making processes. As models enter regulated environments, performance becomes secondary to operational verifiability: systems must demonstrate why they work and how they fail. Anthropic’s behavioural audits — stretching from the Claude 4 System Card to agentic misalignment studies — set a precedent: thousands of adversarial probes that surface hidden autonomy, reward hacking and shutdown resistance. The point is not to rubber-stamp safety but to map the surface of risk so each deviation is observable, classifiable and reproducible. Finance already treats risk as an auditable quantity. Balance sheets are verifiable, processes traceable and metrics reproducible. Applied to AI, the same discipline demands architectures that emit deterministic logs, explicit constraints, measurable privacy budgets and systematic drift detection — so that every decision carries verifiable evidence of its own behaviour.