Semantic Data Loss Prevention and Intelligent Information Governance Across Hybrid Enterprise Infrastructure

The increasing adoption of hybrid enterprise infrastructure has created significant challenges in protecting sensitive information across on-premises data centers, private clouds, public cloud platforms, and distributed applications. Traditional data loss prevention (DLP) solutions primarily depend on predefined rules, keywords, and static classification techniques, which often fail to identify sensitive information when it appears in different formats, contexts, or business workflows. This research proposes a semantic data loss prevention and intelligent information governance framework that integrates natural language processing, machine learning, contextual analysis, and automated policy enforcement to improve enterprise information security. The proposed framework analyzes data content, semantic relationships, user behavior, access patterns, and information sensitivity to identify potential data exposure risks. It incorporates semantic classification, contextual risk assessment, adaptive access control, encryption, and continuous compliance monitoring to protect sensitive information throughout its lifecycle. The framework supports consistent governance across hybrid infrastructure while maintaining data visibility, accountability, and regulatory alignment. Furthermore, intelligent monitoring mechanisms enable organizations to detect unauthorized information transfers, abnormal access activities, and policy violations. The proposed approach establishes a scalable foundation for proactive information protection, reducing dependence on static security rules and strengthening organizational data governance in complex enterprise environments.