Cross-Border Data Privacy and AI Governance: A Comparative Study Between the UK and the US

Abstract

The exponential growth of artificial intelligence (AI) technologies and the increasing globalization of data flows have underscored the urgent need for robust, harmonized data privacy and AI governance frameworks across national borders. This study offers a comparative legal and policy analysis of the United Kingdom (UK) and the United States (US), two influential jurisdictions with contrasting approaches to data protection and AI regulation. While both nations are key players in global technology innovation, their legal architectures differ significantly: the UK adopts a rights-based, comprehensive framework grounded in the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018, whereas the US follows a fragmented, sectoral model that lacks a unified federal data privacy law and relies heavily on state-level regulations and agency guidance, such as the California Consumer Privacy Act (CCPA) and the Federal Trade Commission’s (FTC) oversight. This divergence becomes particularly critical in the context of cross-border data flows, AI model training, and algorithmic accountability, where misaligned standards may generate legal uncertainty, hinder innovation, or risk privacy violations. The study employs a comparative legal methodology, incorporating statutory analysis, policy review, and selected case studies, to assess the convergence and divergence in AI governance structures, enforcement mechanisms, and international data transfer regimes. Findings reveal both challenges and opportunities: although fundamental philosophical and institutional differences persist, recent initiatives such as the UK–US Data Bridge and the US AI Bill of Rights suggest an emerging appetite for transatlantic regulatory interoperability. This paper argues that strategic alignment in data privacy principles and AI risk management could foster trust, legal predictability, and innovation across borders. Policy recommendations are offered to encourage regulatory dialogue, enhance mutual adequacy mechanisms, and support ethical AI development through bilateral cooperation and shared governance models.