Anchoring AI Proof Certificates to Clinical Data Standards: The ARCH Framework for Adaptive Regulatory Compliance and Human Oversight in Clinical Trials

This working paper presents the ARCH Framework — Adaptive Regulatory Compliance and Human Oversight — a field-level implementation specification for anchoring AI proof certificates natively within the CDISC Unified Study Definitions Model (USDM) without requiring new standards or infrastructure. The framework specifies a complete three-gate verification schema: Gate 1 (deterministic regulatory compliance verification), Gate 2 (Lean4 formal structural verification), and Gate 3 (human oversight and attestation). Each gate produces a distinct, cryptographically anchored certificate object with full field-level specifications, conformance rules, controlled vocabularies, and programmatic dependencies. Additional architecture covered includes: risk-based quality management integration anchored to ICH E6(R3) with a four-tier risk classification (HIGH, MODERATE, LOW, MINOR) as the primary architectural primitive; PCCP dynamic ingestion architecture for continuous learning governance without new FDA submissions; retroactive compliance invalidation with risk-calibrated escalation workflows; real-time protocol authoring mode executing Gate 1 and Gate 2 verification in under 100 milliseconds at the moment of human drafting; the Single Pane of Glass user experience for surfacing compliance failures during authoring; biomedical concept verification including the BiomedicalConceptSurrogate Trap; dataset provenance satisfying EU AI Act Article 10; SLA adjudication architecture; living protocol governance; bi-temporal audit trail satisfying 21 CFR Part 11; and multi-jurisdictional adapter architecture. The accompanying Excel data dictionary is the authoritative field-level specification for all attribute definitions, conformance rules, cardinality, and controlled vocabularies. Version 6.0 Working Paper. Technical specification sections are substantially complete. Explanatory narrative sections are in progress. Builds upon Thompson, S. (2026). Toward a Regulatory Validation Framework for AI-Assisted Clinical Trial Activation and Execution.