THREAT ASSESSMENT: Fragmented Global AI Regulation Enables Systemic Risk Without Coordination Mechanisms
When regulatory boundaries outpaced technological reach, prior systems relied on mutual recognition before full harmonization—seven to twelve years passed before alignment emerged, not through treaty, but through repeated procedural trust.
Bottom Line Up Front: The absence of effective cross-border coordination in AI governance creates systemic risks due to regulatory fragmentation, despite the emergence of recognition-based frameworks that could mitigate these dangers without requiring full regulatory harmonization.
Threat Identification: Global AI systems pose transboundary risks—including safety failures, cascading vulnerabilities, and inconsistent compliance standards—amplified by the lack of coordinated regulatory responses across jurisdictions. Current governance structures remain territorially constrained, while AI models are deployed and updated globally in real time, creating dangerous misalignment between risk propagation and regulatory authority (Shlomo-Agon & Davidson, 2026).
Probability Assessment: High likelihood of worsening coordination gaps within 1–3 years (2026–2029), especially as frontier AI systems become more widely integrated into critical infrastructure. Treaty-based solutions are unlikely to close this gap due to their slow negotiation cycles and need for regulatory convergence, which is politically and institutionally infeasible in the near term (Shlomo-Agon & Davidson, 2026).
Impact Analysis: Uncoordinated oversight increases the risk of duplicated assessments, regulatory arbitrage, delayed incident response, and global safety failures. Sectors such as healthcare, finance, and critical infrastructure are particularly vulnerable to cross-border AI incidents that may go undetected or unaddressed due to jurisdictional silos.
Recommended Actions: 1) Accelerate adoption of recognition-based coordination frameworks that allow mutual reliance on foreign safety evaluations and conformity assessments; 2) Strengthen international standards and information-sharing networks to build procedural credibility; 3) Establish reversibility and accountability safeguards to maintain domestic regulatory sovereignty while enabling cross-border cooperation.
Confidence Matrix:
- Threat Identification: High confidence (well-documented in source and observed in current regulatory landscapes)
- Probability Assessment: Medium-High confidence (based on current trajectory of AI deployment and slow pace of treaty formation)
- Impact Analysis: High confidence (supported by analogies to pharmaceuticals and financial regulation)
- Recommended Actions: Medium confidence (dependent on political will and institutional capacity to adopt recognition models)
Citation: Shlomo-Agon, S., & Davidson, O. (2026). Governing AI Through Recognition: Coordination Without Convergence. *Social Science Research Network*.
Published June 11, 2026