AI Security Engineering - Modeling - Detecting - Mitigating New Vulnerabilities

Explore the critical landscape of AI security engineering in this 54-minute RSA Conference talk. Delve into the modeling, detection, and mitigation of new vulnerabilities in AI and machine learning systems. Learn about customer compromise through adversarial machine learning, higher-order bias and fairness concerns, and physical safety and reliability issues stemming from unmitigated security and privacy threats. Examine adversarial audio examples, failure modes in machine learning, and various adversarial attack classifications. Investigate data poisoning attacks on model availability and integrity, and discover proactive defense strategies. Gain insights into threat taxonomy, adversarial goals, and the ongoing race between attacks and defenses. Understand the concept of ideal provable defense and explore security best practices, including defining input/output bounds and threat modeling AI/ML systems. Conclude with an overview of AI/ML pivots to the Security Development Lifecycle (SDL) Bug Bar, equipping you with essential knowledge to protect and defend AI services against emerging threats.