AIRPoC: An AI-Enhanced Blockchain Consensus Framework for Autonomous Regulatory Compliance

Abstract

Following the stablecoin legislation (GENIUS Act) enacted under the second Trump administration in 2025, blockchain has become core digital economy infrastructure. However, privacy risks from decentralization and transparency constrain adoption in regulated industries, requiring solutions that harmonize blockchain architecture with regulatory compliance. Existing research relies on reactive auditing or post-execution rule checking, which wastes computational resources or provides only basic encryption or access controls without comprehensive privacy compliance. The proposed Artificial Intelligence-enhanced Regulatory Proof-of-Compliance (AIRPoC) framework addresses this gap through a two-phase consensus mechanism that integrates AI legal agents with semantic web technologies for autonomous regulatory compliance enforcement. Unlike existing research, AIRPoC implements a dual-layer architecture where AI-powered regulatory validation precedes consensus execution, ensuring that only compliant transactions proceed to blockchain finalization. The system employs AI legal agents that automatically construct and update regulatory databases via multi-oracle networks, using SPARQL-based inference engines for real-time General Data Protection Regulation (GDPR) compliance validation. A simulation-based experimental evaluation conducted across 24 tests with 116,200 transactions in a controlled environment demonstrates 88.9% compliance accuracy, with 9502 transactions per second (TPS) versus 11,192 TPS for basic Proof-of-Stake (PoS) (4.5% overhead). This research represents a paradigm shift to dynamic, transaction-based regulatory models that preserve blockchain efficiency.