Evidence Projection in Multi-Jurisdictional AI Governance

This paper introduces the evidence projection model for multi-jurisdictional AI governance. The paper argues that the next regulatory cycle is converging toward execution-time enforcement evidence rather than after-the-fact documentation, and that conventional parallel compliance architectures fail structurally because multiple governance stacks cannot independently govern the same underlying execution event without divergence between operative enforcement and jurisdiction-specific documentation. The paper proposes an alternative architecture in which a single execution-time authorization boundary produces proof-carrying decision artifacts that project into jurisdiction-specific evidentiary formats. Under this model, jurisdictional fragmentation becomes an evidence-format problem rather than a governance-architecture problem. The paper defines evidentiary projection, explains the structural properties required for projectable authorization artifacts (reproducibility, verifiability, completeness, and boundedness), situates ALLOW / DENY / ABSTAIN verdict semantics within execution-time authorization systems, and maps these verdicts to the testable criteria of the Four Tests Standard (STOP, OWNERSHIP, REPLAY, ESCALATION). It further examines implications for enterprise governance architecture, infrastructure integration, audit consistency, and regulatory scalability across overlapping AI governance regimes. The paper builds on prior FERZ work concerning execution-time authorization, proof-carrying decision artifacts, fail-closed governance, and the distinction between observability, alignment, and authorization. Keywords: AI governance, AI compliance, runtime authorization, execution-time authorization, evidence projection, proof-carrying decision artifacts, fail-closed governance, jurisdictional AI compliance, AI regulatory fragmentation, Four Tests Standard, deterministic AI governance.