This talk introduces a novel approach to building trustworthy AI systems by integrating semantic-aware processing with adaptive security mechanisms. Traditional AI pipelines often suffer from semantic loss during preprocessing, leading to reduced accuracy, misinterpretation, and increased vulnerability to threats.

I present a framework that leverages machine learning techniques, including Random Forest, to detect anomalies, preserve semantic integrity, and enhance trust in data-driven systems. The architecture supports real-time monitoring, intelligent threat detection, and unauthorized access prevention, making it relevant for critical domains such as healthcare and enterprise systems.

Through real-world case studies and experimental results (92% accuracy with reduced semantic loss), this session demonstrates how organizations can move from reactive security to proactive, AI-driven trust frameworks.