Automated Sensitive Data Detection and Masking Framework for Regulated Enterprises

Data security of sensitive information has turned into an urgent issue on the list of businesses that act under the complicated regulatory frameworks, like GDPR, HIPAA, and PCI-DSS. Conventional rule-based data detection and masking techniques are becoming inadequate when it comes to dealing with the volume, heterogeneity and flexibility of the current enterprise data landscape. This review analyzes the development of sensitive data protection frameworks, and the rise of automated, AI-driven products, which are based on machine learning and natural language processing, as well as adaptive governance. The paper introduces a conceptual automated data sensitive data detecting and masking framework, which aims at meeting real-time compliance, scalability, and interpretability. It discusses the essential architectural layers, which are data ingestion, data detection, data classification, data masking, and data governance integration. The existing system comparative analysis proves that hybrid AI methods are superior in terms of accuracy, transparency and compliance agility. Some of the major research issues are identified like model interpretability, multilingual adaptability, and synthetic data generation. This research finds that, with an appropriate solution to explainable AI and a continuous compliance control, automated frameworks would greatly help to secure enterprise data and preserve the operational efficiency and regulatory assurance.