AI-Driven Data Governance Frameworks for Cloud-Native Enterprise Platforms

Abstract

The cloud-native enterprise architecture is driving organisations towards a model shift in the data ecosystem in terms of volume, variety, and velocity. Current data governance models rely on centralised policy enforcement and manual auditing, which is insufficient for modern cloud-native systems where data is distributed across heterogeneous components. This article explores AI-enabled frameworks for data governance in cloud-native enterprise systems, discussing their architectural characteristics, operational mechanisms, and strategic implications. Four interdependent capability layers — AI-based data cataloging and automated lineage, machine-learning-based policy enforcement, scalable integration with containerised pipelines, and AI-enabled decision support — are analysed for their effect on enterprise data pipelines in regulated domains. We introduce an explicit four-layer architecture with a policy feedback loop, three illustrative mini case studies in banking, healthcare, and global multi-cloud governance, and a quantitative impact subsection anchoring estimated 30–50% reductions in compliance violations and hours-to-seconds inline detection-latency improvements to peer-reviewed literature and authoritative industry telemetry.