Securing AI Systems Across Their Lifecycle

As artificial intelligence reshapes industries, it also introduces a unique set of security challenges that extend beyond traditional cybersecurity measures. This course provides the foundational knowledge necessary to build and maintain robust, secure AI systems, safeguarding them against both known and emerging threats. Upon completing this course, you will be equipped to approach AI system development with a security-first mindset, capable of identifying risks and implementing protective measures from the initial design phase through to ongoing operation. What you'll learn: * Understand fundamental AI security concepts and their unique attack surfaces * Identify common vulnerabilities and attack vectors specific to machine learning models * Apply strategies for securing AI training data, model integrity, and deployment infrastructure * Learn principles of responsible AI development, including data privacy and ethical considerations in security * Implement basic practices for detecting and defending against adversarial attacks on AI systems * Develop a comprehensive lifecycle approach to integrate security practices into every stage of AI system development This course begins by establishing core terminology and foundational concepts in AI security, then progresses through practical techniques and best practices for securing various stages of AI system development and operation. This course is designed for beginners with no prior experience in AI security or advanced cybersecurity concepts. Begin your journey to building more resilient and trustworthy AI systems.