Abstract

The life sciences industry handles extremely sensitive master data—patient IDs, proprietary product specs, clinical trial records, and supplier compliance files. Compliance regimes such as HIPAA, GDPR, and FDA 21 CFR Part 11 require strict regimes for data access, masking, and protection. Classic Master Data Governance setups in solutions like SAP MDG deliver accuracy and consistency, yet their rule-based, static approaches to data protection do not keep pace with shifting privacy risks. This paper presents an AI-Enabled Data Protection and Masking Framework designed to sit within life sciences MDG workflows. The framework combines machine learning–based sensitive data classification, context-aware masking, and dynamic real-time access control into the MDG process. Leveraging natural language processing and pattern recognition, the solution autonomously detects sensitive columns—such as patient IDs, trial site information, and controlled-substance data—then applies masking, tokenization, or encryption based on user role, geographical location, and applicable regulatory jurisdiction. In a controlled simulation, we tested the framework and saw a drop in potential data exposure risks of more than 30%, a boost in readiness for compliance audits, and a simplification of the approval process. Results indicated that embedding AI into the Master Data Governance layer strengthens both privacy and security, yet keeps the data fit for analytics and operational choices. Life sciences companies thus gain the ability to meet regulatory demands without stifling innovation.

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