AI-AUGMENTED RISK DETECTION IN CYBERSECURITY COMPLIANCE: A GRC-BASED EVALUATION IN HEALTHCARE AND FINANCIAL SYSTEMS

Abstract

This study provides a comprehensive review of recent advancements in artificial intelligence (AI) applied to regulatory automation, particularly in highly regulated sectors such as healthcare and finance. Drawing upon a synthesis of contemporary literature and cross-sectoral analyses, the research aims to confirm AI's foundational role in compliance, assess the evolutionary progression of AI models in this domain, and compare their integrative functions across different organizational environments. Traditional compliance frameworks have long relied on manual audits, rule-based systems, and static regulatory databases, often resulting in inefficiencies, delays, and increased operational risks. These systems are increasingly augmented by machine learning and natural language processing (NLP), enabling them to interpret complex policy texts, flag anomalies, and implement mitigations autonomously. Importantly, the study also examines implementation variations by sector. For instance, healthcare systems prioritize ethical oversight and data sensitivity, employing federated learning and explainable AI to maintain compliance with HIPAA and GDPR. Financial institutions, by contrast, emphasize biometric verification, internal governance optimization, and real-time risk analytics tailored to high-volume transaction environments. The outcome evaluation phase of the review validates the real-time adaptability of these systems and underscores their capacity for seamless integration into Governance, Risk, and Compliance (GRC) architectures. Ultimately, this research illustrates that AI is not merely enhancing compliance but fundamentally transforming how institutions govern risk, uphold accountability, and ensure regulatory alignment.