Inference Time Law

Inference Time Law is the idea that high stakes AI governance cannot rely on training time intentions or probabilistic safety layers. If enforcement only exists as prompts, model side filters, or post hoc review, it can fail under contradiction, drift, or adversarial pressure. Governance that must be provable needs an enforcement layer that operates at inference time, before the model processes the request. This publication describes what that looks like in practice, including deterministic pre inference gating, explicit policy evaluation, and refusal reasons that can be audited. It also explains why evidence matters, including the role of cryptographically signed artefacts that can be verified offline. The goal is simple. Move governance from claims to controls, and from controls to evidence. If you want AI systems to be governable, auditable, and insurable, you need enforcement that is deterministic, testable, and repeatable under real conditions.