AI-Enabled Information Governance for Healthcare Productivity Systems: Addressing Limitations of Traditional Data Loss Prevention

Abstract

Enterprise productivity platforms—including email, collaboration tools, and document repositories—are increasingly used within healthcare organizations to support clinical coordination and administrative communication. As protected health information (PHI) flows beyond traditional electronic health record (EHR) environments, conventional data loss prevention (DLP) systems based on static pattern matching and rules-based policies struggle to capture the contextual and semantic characteristics of healthcare communication. These limitations create governance gaps in environments where clinical information is embedded within free text, operational workflows, and cross-platform collaboration channels.

This study analyzes the limitations of traditional DLP approaches in healthcare productivity environments and proposes an AI-enabled information governance architecture designed to address contextual sensitivity and workflow variability. The framework integrates natural language processing for semantic classification of healthcare discourse, behavioral analytics for detecting anomalous access patterns, and contextual risk assessment based on role, workflow metadata, and communication context. By combining multi-platform data ingestion, semantic classification, adaptive policy orchestration, and immutable audit logging, the proposed architecture supports risk-adjusted enforcement while preserving clinical workflow continuity.

Drawing on empirical findings from prior research in clinical text de-identification, insider-threat detection, and healthcare data governance, the paper demonstrates how machine learning-based classification models outperform rule-based systems in detecting PHI and reducing false positives that disrupt operational workflows. The proposed governance framework provides a scalable model for protecting sensitive health information across heterogeneous productivity environments while maintaining regulatory compliance under frameworks such as HIPAA and GDPR. The findings contribute to the design of context-aware governance architectures capable of supporting the growing integration of AI-enabled productivity platforms in healthcare systems.