Intelligent regulatory monitoring and audit trail information systems for enterprise risk and compliance management

This paper proposes an intelligent enterprise compliance and audit-trail management framework for organizational governance and regulatory monitoring. The research integrates AI-assisted policy analysis, operational risk documentation, compliance verification systems, and digital evidence management for enterprise-scale institutions. The framework improves transparency, accountability, and governance automation across regulated operational environments. The motivation for this work arises from the growing complexity of enterprise regulations and the limitations of fragmented compliance systems that fail to provide real-time monitoring and end-to-end audit traceability. To address this issue, the study adopts a Design Science Research approach and integrates artificial intelligence, process mining, and blockchain technology into a unified compliance architecture. The system continuously processes enterprise event data, detects anomalies using hybrid AI models, reconstructs workflows through process mining, and ensures tamper-proof audit records via blockchain-based logging. The framework was evaluated using simulated enterprise datasets representing operational and regulatory environments. Results show strong performance in compliance detection, anomaly identification, workflow verification, and audit integrity with low monitoring latency. The findings demonstrate that the proposed system enhances real-time governance, improves risk visibility, and supports proactive decision-making in modern enterprise ecosystems.