Course Description
As artificial intelligence (AI) and machine learning (ML) become increasingly central to our lives, ensuring their security and resilience is more crucial than ever. This course is designed for ML engineers, data scientists, AppSec and InfoSec practitioners, and business leaders who are committed to securing their AI/ML systems and building a safer AI-powered world.
Course author, Diana Kelley, CISO at Protect AI, is joined by several of her expert colleagues to guide learners through a comprehensive exploration of the most critical security challenges in AI and ML. Using the MLSecOps framework, you’ll learn effective strategies to mitigate these risks. Discover how the MLOps lifecycle intersects with DevSecOps, identifying key points where security can be seamlessly woven into the ML pipeline.
Throughout the course, learners will discover how to secure machine learning models, conduct AI-specific risk assessments, audit and monitor supply chains, implement incident response plans, and build a high-performing MLSecOps team. By the end, they’ll be prepared to help individuals and organizations proactively secure their AI and ML systems against evolving threats.