Artificial Intelligence–Driven Metadata Management for Comprehensive Data Governance

Abstract

As data ecosystems become increasingly complex, organizations are compelled to implement strong data governance mechanisms to safeguard data accuracy, confidentiality, and regulatory adherence. Within this landscape, metadata management serves as a fundamental component by enabling the systematic organization, classification, and retrieval of data across diverse platforms. However, conventional metadata management techniques often fall short in terms of scalability, flexibility, and responsiveness to real-time data demands. This study presents an AI-driven metadata management framework that employs machine learning (ML) and natural language processing (NLP) to automate metadata categorization, contextual interpretation, and adaptive policy enforcement. By integrating AI capabilities, the proposed approach enhances the precision, performance, and scalability of metadata governance within heterogeneous data environments. Experimental findings indicate notable improvements in data discoverability, efficient lineage tracing, and sustained compliance with regulatory requirements. The proposed framework offers a comprehensive solution for modern enterprise data governance by transforming metadata management into an intelligent, adaptive, and proactive function. Ultimately, the study highlights the transformative role of AI in strengthening decision-making processes, improving data security, and ensuring continuous regulatory compliance within evolving organizational data infrastructures.