AI-Driven Data Governance Frameworks: Enhancing Data Accuracy, Compliance, and Operational Efficiency

Abstract

In the contemporary digital economy, organizations increasingly depend on data as a strategic asset for decision-making, innovation, and regulatory compliance. As a result, data governance has emerged as a critical organizational function that ensures data is accurate, secure, accessible, and ethically managed. The growing complexity, volume, and diversity of data sources have intensified challenges related to data quality, regulatory compliance, and risk management. Traditional governance approaches, which rely heavily on manual processes and rule-based systems, often struggle to scale effectively and remain error-prone in dynamic data environments. Recent advancements in artificial intelligence (AI) and machine learning (ML) present transformative opportunities to modernize data governance frameworks. AI-driven models enable automation of key governance functions such as data validation, anomaly detection, privacy monitoring, and compliance enforcement. By reducing human intervention, these technologies enhance accuracy, improve real-time monitoring, and ensure adherence to regulatory requirements such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This review critically examines the role of AI and ML in strengthening data governance practices, with particular emphasis on improving data accuracy, compliance, and operational efficiency. Using a systematic PRISMA-based methodology, recent academic studies are analyzed to evaluate AI applications in data quality management, security, privacy, and compliance monitoring. The findings demonstrate that AI-powered governance frameworks significantly reduce data errors, enhance scalability, and enable proactive compliance management. However, challenges such as ethical concerns, data bias, transparency, and system integration remain. Overall, this study highlights the growing importance of AI-driven data governance as a foundation for secure, compliant, and reliable data management across industries.