🤖 AI Summary
This paper identifies a critical regulatory lag and fragmentation arising from the deep integration of AI into essential digital infrastructure—particularly telecommunications—where existing telecommunications, cybersecurity, and data protection laws largely overlook AI-specific risks such as model drift, algorithmic bias, and decision opacity; notably, AI systems in telecom remain virtually unregulated. Employing a comparative legal methodology, the study systematically analyzes policy documents from ten jurisdictions, synthesizing insights across AI governance, telecommunications law, and data protection theory. Its core contribution is the identification of “institutional coordination failure” as a fundamental governance gap, and the proposal of the first cross-sectoral, forward-looking regulatory framework tailored to AI-enabled digital infrastructure. This framework shifts governance from reactive compliance toward proactive, systemic integration, offering both theoretical foundations and actionable institutional design pathways for building unified, adaptive AI infrastructure regulation.
📝 Abstract
As Artificial Intelligence (AI) becomes increasingly embedded in critical digital infrastructure, including telecommunications, its integration introduces new risks that existing regulatory frameworks are ill-prepared to address. This paper conducts a comparative legal study of policy instruments across ten countries, examining how telecom, cybersecurity, data protection, and AI laws approach AI-related risks in infrastructure. The study finds that regulatory responses remain siloed, with minimal coordination across these domains. Most frameworks still prioritize traditional cybersecurity and data protection concerns, offering limited recognition of AI-specific vulnerabilities such as model drift, opaque decision-making, and algorithmic bias. Telecommunications regulations, in particular, exhibit little integration of AI considerations, despite AI systems increasingly supporting critical network operations. The paper identifies a governance gap where oversight remains fragmented and reactive, while AI reshapes the digital infrastructure. It provides a foundation for more coherent and anticipatory regulatory strategies spanning technological and institutional boundaries.