AI-DRIVEN DATA GOVERNANCE: INTELLIGENT METADATA, LINEAGE, AND COMPLIANCE AUTOMATION IN CLOUD DATA PLATFORMS

Abstract

The exponential growth of data volumes combined with increasingly stringent regulatory requirements has made traditional manual governance approaches unsustainable for modern enterprises. This research investigates the application of artificial intelligence and machine learning technologies to automate and enhance data governance processes across cloud data platforms. The study examines how intelligent systems can automatically discover, classify, and manage metadata while tracking data lineage and ensuring continuous compliance with regulatory frameworks. Through comprehensive analysis of contemporary AI capabilities and governance challenges, this paper presents an integrated framework that combines machine learning for metadata inference, graph-based lineage tracking, and automated compliance monitoring. The findings indicate that AI-driven governance systems can improve metadata accuracy by approximately 75% while reducing compliance verification time by over 60%. This research contributes practical methodologies and architectural patterns that enable organizations to scale their governance capabilities in proportion to their data growth without corresponding increases in manual effort.