Model risks Playbook for Teams

RCCE students will learn machine learning model security risks including adversarial attacks, model poisoning, model theft, model inversion, and membership inference attacks. RCCE students will learn to assess ML model security throughout the model lifecycle from training through deployment, identify vulnerabilities in model architectures and training pipelines, detect adversarial input attacks designed to cause misclassification, prevent model poisoning through training data integrity controls, protect model intellectual property against extraction attacks, implement model monitoring for drift and adversarial behavior, and develop incident response procedures for compromised ML models. This team-oriented course builds collaborative workflows and organizational playbooks for security operations. Starting from foundational concepts, RCCE students will learn to create and implement standardized procedures that enable consistent performance across team members and shifts. Students develop the documentation, communication, and coordination skills needed for effective team-based security operations.