Cross-Border Data Governance for Government AI Training

Abstract

Background: Cross-Border Data Governance for Government AI Training has become a practical governance concern because differences in institutional design and administrative capacity can shape how policy is applied in practice.

Objective: This study assessed the implementation of AI training data, focusing on consistency, institutional conditions, and outcomes in relation to cross-border data.

Methods: The study compared 6 jurisdictions or institutional settings using legal and policy documents, structured organizational evidence, and where relevant key-informant material. A common framework examined authority, coordination, transparency, procedural safeguards, enforcement, and implementation capacity.

Results: Three recurring implementation patterns emerged for AI training data. Higher-performing settings combined explicit mandates, formal coordination, and accessible review arrangements; together, these features were associated with a 30% improvement in the composite implementation score. Fragmented responsibility, by contrast, was linked to delay and inconsistent enforcement.

Conclusion: The evidence on AI training data shows that policy design and administrative execution cannot be separated. Accordingly, public-sector AI rules are unlikely to be effective without operational capacity, transparent oversight, and clearly assigned responsibility.

Study design: comparative policy and compliance study.

Publication history: Originally published in print on 15 June 2026, Volume 6, Article 16. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.