Legal Frameworks for Combating Deepfake-Driven Fraud in Autonomous Systems

Abstract

Deepfake technology has emerged as a critical legal threat to autonomous systems worldwide, yet Uzbekistan's existing legal framework remains fundamentally unprepared to address it. This research examines the adequacy of Uzbekistan's Criminal Code, Cybersecurity Law of 2022, and Personal Data Law of 2019 in combating deepfake-driven fraud targeting autonomous systems. The central research question asks to what extent these laws provide sufficient legal protection and where they critically fail. Employing a qualitative methodology combining doctrinal legal analysis and document analysis, this research systematically evaluates Uzbekistan's legislative gaps through comparative examination of legal frameworks from the United States, the European Union, China, and South Korea. Official legal texts were retrieved from Lex.uz, while scholarly sources were drawn from peer-reviewed legal databases. Findings reveal significant deficiencies across five critical areas: criminal law, cybersecurity legislation, civil liability, digital evidence standards, and biometric data protection. This research proposes dedicated synthetic media legislation, targeted amendments to existing laws, certified forensic evidentiary standards, enhanced biometric data protections, and establishment of an independent AI regulatory body, offering Uzbekistan a concrete and achievable legal reform roadmap suited to its institutional capacity and legal tradition.