The rapid expansion of enterprise data within cloud-native ecosystems has greatly heightened the intricacy of data governance, compliance management, metadata management, and lineage tracking. Conventional governance frameworks, which rely heavily on manual supervision, rule-based controls, and periodic audits, are becoming increasingly insufficient for contemporary cloud platforms such as Microsoft Fabric, Azure Data Lake, Databricks, and hybrid enterprise architectures. This research introduces an AI-Driven Autonomous Data Governance Framework (AIDAGF) that leverages Large Language Models (LLMs), intelligent AI agents, metadata intelligence, automated data cataloging, AI-driven compliance monitoring, and data lineage automation to create self-healing, self-governing enterprise data ecosystems. The framework merges governance automation with explainable artificial intelligence (XAI), facilitating ongoing compliance validation, anomaly detection, schema drift correction, and policy enforcement across distributed cloud environments. The study employs a qualitative desk-review methodology, bolstered by architecture synthesis and enterprise case analysis, to assess the applicability of the proposed model. A scenario based on Microsoft Fabric is provided to illustrate practical feasibility. The findings indicate that autonomous governance significantly enhances data quality, governance agility, regulatory compliance, and operational efficiency while minimizing reliance on humans and governance latency. This research offers a novel enterprise-ready governance architecture tailored for modern digital ecosystems and outlines a strategic roadmap for organizations aiming for intelligent data governance transformation.
Tammiraju et al. (2026) studied this question.