What does this research mean for the field?

Multi-jurisdictional AI regulatory fragmentation can be resolved by implementing a single execution-time authorization boundary that generates tamper-evident artifacts projectable into jurisdiction-specific formats, overcoming the structural failures of conventional parallel compliance architectures. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

The aim is to develop a model that addresses regulatory complexities in multi-jurisdictional AI governance through evidence projection.

July 8, 2026Open Access

Evidence Projection in Multi-Jurisdictional AI Governance

Key Points

The aim is to develop a model that addresses regulatory complexities in multi-jurisdictional AI governance through evidence projection.
Proposed a new architecture for execution-time enforcement and evidence projection in AI governance.
Defined structural properties for projectable authorization artifacts, including verifiability and completeness.
Explored the integration of existing governance standards within the proposed model.
Proposed architecture mitigates regulatory fragmentation by treating evidence formatting as a key issue.
Defined properties create a framework that ensures robust authorization and compliance for AI systems.
Identified implications for enhancing audit consistency and scalability across overlapping AI governance regimes.

Abstract

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 tamper-evident authorization artifacts, implemented as Proof-Carrying Decisions under the Five Tests Standard (5TS), 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, established input origin, completeness relative to anticipated projections, and boundedness. It situates ALLOW / DENY / ABSTAIN verdict semantics within execution-time authorization systems and maps those semantics to the conformance requirements of the Five Tests Standard: STOP, OWNERSHIP, REPLAY, ESCALATION, and PROVENANCE. Provenance is treated as origin, not truth. The paper further examines implications for enterprise governance architecture, infrastructure integration, audit consistency, and regulatory scalability across overlapping AI governance regimes. It builds on prior FERZ work concerning execution-time authorization, tamper-evident authorization artifacts, fail-closed governance, and the distinction between observability, alignment, and authorization. Keywords: AI governance, AI compliance, runtime authorization, execution-time authorization, evidence projection, tamper-evident authorization artifacts, fail-closed governance, jurisdictional AI compliance, AI regulatory fragmentation, Five Tests Standard, deterministic AI governance, authorization artifact, independently reconstructable verdict.

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