Runtime Evidence

Runtime evidence refers to records captured while an AI system is actually operating, such as logs of prompts, outputs, tool calls, data accessed, and decisions made in production. For governance teams, this is the material that shows what a system really did, as opposed to what its documentation says it should do. It matters because regulators, auditors, and litigators increasingly ask for proof of live behavior when assessing accountability, incident response, and compliance with obligations under frameworks like the EU AI Act.

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ResearchGlobal2026-11-03

1,000+ AI Rules, One Audit Gap: Runtime Evidence Is What Regulators Want

Openlayer's Country-by-Country AI Regulation Guide maps more than 1,000 AI policy initiatives across 72 countries. It finds that regulators worldwide are converging on the same core requirements: risk classification, human oversight, transparency, post-market monitoring, and version-linked documentation. The critical finding for compliance teams is that audit readiness now requires evidence that controls operated during live production use, not just that they were designed.

Openlayer multi-jurisdiction compliance audit readiness risk classification AI regulation post-market monitoring runtime evidence