The global regulatory landscape is currently undergoing a period of unprecedented volatility, characterized by the introduction of complex frameworks such as GDPR, CCPA, HIPAA, and the evolving EU AI Act. For modern enterprises, manual compliance monitoring—once the standard for risk management—is no longer a viable strategy due to the sheer volume, variety, and velocity of data generated across distributed digital ecosystems. This review examines the paradigm shift toward AI-powered compliance monitoring systems, which leverage Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision to provide real-time, continuous oversight. By automating the ingestion and interpretation of legal texts and cross-referencing them with internal operational telemetry, these systems identify \\\"compliance gaps\\\" before they manifest as legal liabilities. This article categorizes current methodologies, including the use of Large Language Models (LLMs) for semantic policy mapping and Deep Learning for detecting anomalous financial patterns indicative of money laundering or fraud. We explore how AI mitigates \\\"regulatory fatigue\\\" by filtering noise and highlighting high-priority risks, thereby allowing compliance officers to transition from administrative data processors to strategic advisors. Furthermore, the review addresses the critical challenges of algorithmic bias, the \\\"black-box\\\" nature of deep neural networks, and the necessity for Explainable AI (XAI) in regulatory reporting. By synthesizing recent academic research and industrial case studies, this paper provides a strategic roadmap for building \\\"compliance-by-design\\\" architectures. The findings suggest that AI-powered systems not only reduce the cost of adherence but also foster a culture of transparency and proactive ethical governance.
Kiran Das (2023) studied this question.