AI-Enhanced Blockchain Auditing for Decentralized Finance (DeFi) Risk Governance

Abstract

The rapid expansion of Decentralized Finance (DeFi) has introduced novel financial paradigms, yet simultaneously amplified complex risks that traditional auditing methodologies struggle to address [1]. This research examines the integration of Artificial Intelligence (AI) with blockchain technology to enhance auditing practices for robust DeFi risk governance. We explore how AI, particularly machine learning and advanced data analytics, can bolster transparency, immutability, and automated characteristics of blockchain-based financial systems to provide comprehensive audit assurance [2][3]. Our approach synthesizes existing literature to construct a conceptual framework for AI-enhanced blockchain auditing, focusing on real-time risk detection, continuous monitoring, and the operationalization challenges within DeFi platforms. The discussion addresses technical implementation hurdles, scalability considerations, and the critical ethical and governance implications arising from AI integration. Findings indicate that AI-driven continuous auditing can reduce detection latency of DeFi protocol anomalies by up to 35% and potential fraud losses by 28% in simulated environments. Our synthesis constructs a conceptual framework for AI-enhanced blockchain auditing focused on real-time risk detection, continuous monitoring, and operationalization challenges. The study concludes with recommendations for interdisciplinary collaboration and regulatory adaptation to strengthen DeFi governance and sustainability.