AI-Powered Data Governance: Advancing Privacy and Compliance Through Data Masking and Sentiment Analysis

Abstract

The integration of artificial intelligence in data governance has revolutionized how organizations protect and manage sensitive information while ensuring regulatory compliance. Advanced data masking techniques combined with sentiment analysis capabilities have established new standards in privacy protection and compliance monitoring. The implementation of comprehensive frameworks incorporating static and dynamic data masking, coupled with real-time sentiment analysis, has significantly enhanced organizations' ability to detect and prevent compliance violations. Modern architectural considerations focusing on scalability, security, and resource optimization have enabled robust implementation of AI-driven governance solutions. The emergence of enhanced privacy technologies, including homomorphic encryption and federated learning systems, alongside explainable AI frameworks and edge computing integration, has transformed traditional data protection approaches. These technological advancements have led to substantial improvements in data security, compliance monitoring efficiency, and overall governance effectiveness while reducing operational complexities and associated costs.