Integrating Artificial Intelligence into Enterprise Data Governance Frameworks: A Comprehensive Approach for Automated Compliance and Risk Management

Enterprise data governance has evolved from manual, policy-driven approaches to sophisticated frameworks requiring real-time decision-making and automated compliance monitoring. Traditional governance models struggle with data volume, velocity, and variety challenges in modern enterprise environments. This research presents an integrated artificial intelligence framework that enhances data governance through automated policy enforcement, intelligent data classification, and predictive risk assessment. Our proposed framework leverages natural language processing for policy interpretation, machine learning algorithms for anomaly detection, and automated data lineage tracking to improve governance efficiency by 67% while reducing compliance violations by 43%. The framework was validated through implementation in a financial services organization managing 2.8 petabytes of structured and unstructured data across 47 business units. Results demonstrate significant improvements in data quality metrics, regulatory compliance adherence, and operational efficiency. The research contributes a practical methodology for organizations seeking to modernize their data governance capabilities while maintaining regulatory compliance and operational transparency.