AI Security Measures

The book explores methods for securing AI systems, from understanding machine learning principles to hardening models against attacks using techniques like adversarial training and anomaly detection. For example, the text examines how publicly available datasets demonstrate both the exploitation and mitigation of vulnerabilities. Furthermore, the book investigates data sanitization and privacy-preserving techniques to safeguard training data.

Progressing from foundational concepts, the book details AI threats, model hardening, data security, and deployment monitoring. This approach provides AI developers, cybersecurity professionals, and IT managers with actionable insights to build more resilient AI solutions within the realms of AI and Semantics and Information Technology.