Privacy-Centric AI Systems for Identifying and Securing Sensitive Information

As data expands rapidly across multi-cloud, edge, and hybrid environments, maintaining privacy has become an increasingly dynamic challenge. Traditional rule-based protection models fail to adapt to the speed, scale, and contextual diversity of modern data flows. This study presents an AI-centric privacy architecture built to detect, classify, and safeguard sensitive data such as personally identifiable information (PII), health records, and financial details across distributed infrastructures. The framework integrates machine-learning-driven discovery, real-time remediation, and federated learning, enabling autonomous model updates without compromising local data ownership. Privacy enforcement modules—deployed as containerized microservices—perform inline operations like encryption, masking, and policy enforcement directly at ingestion points across clouds and edge nodes. Experimental outcomes show accuracy levels between 94–97% with response times under 120 ms, fully aligned with global compliance standards including GDPR, HIPAA, and CCPA. The architecture scales seamlessly through MLOps pipelines, ensuring enterprise integration with minimal manual oversight. This work underscores the need for context-aware, self-evolving AI systems that embed ethical and regulatory intelligence into every layer of distributed data processing.