Protecting Sensitive Data in AI-Driven Critical Infrastructure: Where Existing Privacy Controls Fall Short - Seguridad Mania.com - España y América Latina

The session concludes with a forward looking perspective on how privacy controls must evolve to better protect sensitive data in high impact systems. It emphasizes a shift toward context aware, system level, and interaction focused approaches that align with how AI is actually used in critical infrastructure. Key Takeaways: • Understand where privacy controls fail in AI-driven critical infrastructure Identify common breakdown points between policy, design, and real world system behavior • Recognize new data exposure pathways introduced by AI Learn how AI-driven processing, integrations, and user interactions create risks not addressed by traditional controls • Evaluate limitations of existing data protection mechanisms Understand why classification, masking, and data loss prevention are insufficient in high impact AI enabled environments • Gain insight into protecting sensitive data in critical systems Explore practical considerations for securing data across complex and distributed infrastructure • Learn how privacy controls must evolve for AI environments Shift from static and perimeter based controls to context aware and interaction level enforcement strategies