Model risks Incident Handling: Primer
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 incident response course prepares students to act decisively during security incidents with structured workflows and clear decision frameworks. Starting from foundational concepts, RCCE students will learn containment, evidence collection, eradication, and recovery procedures specific to this domain. Students practice incident scenarios that build the composure, coordination, and documentation skills essential for effective incident handling.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing Model risks Incident Handling: Primer
- Design a scalable privilege management architecture with policy and enforcement
- Execute hands-on tasks for learning objectives
- Execute hands-on tasks for core competencies
- Build detections and response workflows for privilege escalation, including Course Outcome.
- Execute hands-on tasks for threat volume
- Execute hands-on tasks for regulatory pressure
- Execute hands-on tasks for defense imperative — covering 77% of AI orgs report incidents, EU AI Act mandates risk controls.
- Execute hands-on tasks for training data
- Design a scalable privilege management architecture with policy and enforcement, including Weight extraction, and Model Artifacts.
- Execute hands-on tasks for adversarial attack fundamentals
| Module 01 | Model Risks Incident Handling: Primer |
| Module 02 | Learning Objectives |
| Module 03 | Core Competencies |
| Module 04 | Incident Response Skills |
| Module 05 | ML Model Security Landscape |
| Module 06 | Threat Volume |
| Module 07 | Regulatory Pressure |
| Module 08 | Defense Imperative |
| Module 09 | Model Lifecycle Security Framework |
| Module 10 | Training Data |
| Module 11 | Model Parameters |
| Module 12 | Adversarial Attack Fundamentals |
| Module 13 | White-Box Attacks |
| Module 14 | Attack Goals |
All hands-on labs run on Rocheston Rose X OS. Students practice model risks incident handling: primer by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Design a scalable privilege management architecture with policy and enforcement
- Lab 2: Execute hands-on tasks for learning objectives
- Lab 3: Execute hands-on tasks for core competencies
- Lab 4: Build detections and response workflows for privilege escalation
- Lab 5: Design a scalable privilege management architecture with policy and enforcement
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Model risks Incident Handling: Primer, verifiable through the Rocheston certification portal.
- Full access to all course materials and slide decks
- Hands-on lab access on Rocheston Rose X OS environment
- Access to Rocheston CyberNotes
- Access to Rocheston Zelfire — EDR/XDR SIEM platform
- Access to Rocheston Raven — online cyber range exercise platform
- Access to Rocheston Vulnerability Vines AI