Authors: Vasudev Sharma
Abstract: The proliferation of enterprise data across multi-cloud infrastructures has intensified the complexity of managing data integrity, regulatory compliance, and interoperability in distributed ecosystems. This study introduces a cognitive data governance pipeline designed to autonomously orchestrate, monitor, and optimize governance functions across heterogeneous enterprise platforms such as SAP HANA, Oracle Autonomous Database, and cloud-native storage systems. Unlike conventional rule-based frameworks, the proposed model leverages deep learning, semantic reasoning, and federated policy orchestration to build adaptive feedback loops that detect inconsistencies, predict compliance deviations, and self-correct data governance pathways in real time. The architecture integrates an explainable intelligence layer that interprets anomaly behaviors, traces root causes, and supports transparent auditability without human intervention. Through simulated deployments in hybrid cloud environments, the framework demonstrates measurable improvements in compliance latency, metadata synchronization, and decision throughput, achieving over 35 percent higher efficiency in governance verification and 25 percent reduction in manual remediation efforts. The cognitive design transforms governance from a static, policy-driven function into a dynamic, self-learning system capable of aligning continuously with regulatory and operational shifts. The findings advance the notion of autonomous governance intelligence as a foundational component of modern multi-cloud data ecosystems, offering enterprises a sustainable pathway toward resilient, compliant, and self-regulating digital operations.
