Authors: Professor Henry Bailey, Professor Charles Anderson, Edward Parker, Chaitanya Srinivas, Yashwanth kumar
Abstract: The rapid growth of enterprise data across cloud, hybrid, and distributed computing environments has created significant chal-lenges in maintaining effective data governance, security, quality, compliance, and operational efficiency. Traditional data gov-ernance approaches frequently depend on manual processes, predefined policies, and centralized decision-making, which can limit their ability to respond dynamically to continuously changing data environments. This research proposes an AI-Driven Framework for Autonomous Enterprise Data Platform Governance that integrates artificial intelligence, machine learning, metadata management, automated policy enforcement, data quality monitoring, lineage analysis, and intelligent decision-making to establish a more adaptive governance model. The proposed framework continuously analyzes enterprise data assets, metada-ta, access patterns, quality indicators, and governance policies to identify risks, detect anomalies, recommend corrective actions, and automate governance activities. AI-driven agents support automated metadata classification, policy validation, compliance monitoring, data quality assessment, and lifecycle management while maintaining human oversight for high-impact decisions. The framework is designed around interoperable governance layers that connect data discovery, security, privacy, quality, line-age, compliance, and platform operations. A conceptual evaluation model is proposed using metrics such as governance auto-mation rate, policy compliance accuracy, anomaly detection accuracy, metadata completeness, data quality improvement, re-sponse time, and operational overhead. The proposed approach aims to reduce manual governance effort while improving transparency, consistency, scalability, and responsiveness across enterprise data platforms. The research demonstrates how autonomous and AI-enabled governance can provide a foundation for next-generation enterprise data platforms capable of continuously adapting to evolving business, regulatory, and technological requirements.
