Model risks Incident Response
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 Response
- Design a scalable privilege management architecture with policy and enforcement
- Build detections and response workflows for privilege escalation
- Execute hands-on tasks for learning objectives — covering Identify adversarial attack categories.
- Design a scalable privilege management architecture with policy and enforcement, including Identify adversarial attack categories.
- Monitor and audit privilege usage; detect escalation attempts, including model monitoring controls.
- Execute hands-on tasks for operational skills — covering Practice structured IR workflows.
- Execute hands-on tasks for core concept — covering Mathematical function trained on data.
- Execute hands-on tasks for common types — covering Neural networks and deep learning.
- Execute hands-on tasks for data collection
- Explain ML Model Attack Surface Overview fundamentals
| Module 01 | ML Model Risks |
| Module 02 | Incident Response |
| Module 03 | Learning Objectives |
| Module 04 | ML Model Security |
| Module 05 | Defense & Monitoring |
| Module 06 | Operational Skills |
| Module 07 | What Is a Machine Learning Model |
| Module 08 | Core Concept |
| Module 09 | Common Types |
| Module 10 | ML Model Lifecycle Security |
| Module 11 | Data Collection |
| Module 12 | ML Model Attack Surface Overview |
| Module 13 | Training Phase |
| Module 14 | Deployment Phase |
All hands-on labs run on Rocheston Rose X OS. Students practice model risks incident response 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: Build detections and response workflows for privilege escalation
- Lab 3: Execute hands-on tasks for learning objectives
- Lab 4: Design a scalable privilege management architecture with policy and enforcement
- Lab 5: Monitor and audit privilege usage; detect escalation attempts
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Model risks Incident Response, 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