Autonomous Multi-Jurisdictional Logistics under 21st Century Trade Fragmentation

Abstract

Purpose: This study develops an operating model for autonomous, multi-jurisdictional freight flows that must simultaneously meet requirements for customs risk management, cybersecurity resilience, AI governance, and cross-border data stewardship, addressing the compliance and governance challenges created by the convergence of trade fragmentation and autonomous logistics deployment.

Methodology: A multi-case, document-based comparative analysis was applied, anchored in recent EU border-risk reforms (ICS2 Release 3), security baselines (WCO SAFE), and horizontal regulatory regimes (NIS2, the EU AI Act, and eIDAS 2.0). Documents were coded using a structured scheme covering data requirements, trust and identity expectations, and operational governance mechanisms.

Findings: The study synthesizes an evidence-first compliance architecture mapping: (i) data elements required for advance cargo security filings; (ii) cryptographic identity and audit trails for automated decisions; and (iii) operational escalation protocols to preserve safety and service continuity under disruption. Corridors implementing decision-level audit trails exhibit lower documentation defect rates and shorter exception cycle times.

Contribution to Theory, Policy and Practice: The paper contributes a practical governance blueprint that is role-segregated, auditable, and interoperable across regulators, alongside a framework for measuring compliance latency and exception handling in autonomous logistics. It enables managers to quantify risk, reduce clearance variability, and maintain uptime in safety-critical delivery networks, while extending compliance theory from episodic documentation to continuous, architecture-embedded assurance.