AI Security Measures addresses the critical intersection of artificial intelligence and cybersecurity, focusing on the vulnerabilities within AI models and strategies to protect data. It highlights how AI systems, while powerful, are susceptible to adversarial attacks and data poisoning, potentially leading to severe consequences in sectors like finance and healthcare. This book uniquely emphasizes a proactive, defense-in-depth approach, advocating that AI security should be integral to the AI development lifecycle, rather than an afterthought.
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.
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