AI incident response for Beginners
RCCE students will learn incident response procedures for AI-related security events including AI system compromise, model manipulation, training data breaches, and AI output abuse. RCCE students will learn to identify and classify AI security incidents, apply containment strategies specific to AI systems including model isolation and rollback, collect AI-specific forensic evidence including model versions, training data, and inference logs, investigate root causes of AI incidents, coordinate response efforts between AI engineering, security, and legal teams, develop AI-specific incident response playbooks, and conduct post-incident analysis to improve AI system security. Designed for students with no prior experience in this area, this course builds knowledge from the ground up with clear explanations, guided demonstrations, and progressive skill-building. Building on core knowledge, RCCE students will learn core concepts through practical examples that connect theory to real-world security operations. By completion, students will have the foundational knowledge and hands-on confidence needed to contribute in professional cybersecurity roles.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing AI incident response for Beginners
- Explain Course Overview fundamentals
- Execute hands-on tasks for what you will learn
- Execute hands-on tasks for familiarity with general ir concepts a plus — covering Course Structure.
- Execute hands-on tasks for hands-on confidence with ai security events
- Execute hands-on tasks for key drivers for ai ir — covering AI systems make autonomous decisions with real-world impact.
- Execute hands-on tasks for attack surface
- Design a scalable privilege management architecture with policy and enforcement
- Execute hands-on tasks for evidence types
- Monitor and audit privilege usage; detect escalation attempts, including Training data repositories and lineage.
- Execute hands-on tasks for ir touchpoints — covering Data validation checkpoints.
- Execute hands-on tasks for ai security threat landscape
| Module 01 | Course Overview |
| Module 02 | What You Will Learn |
| Module 03 | Familiarity with general IR concepts a plus |
| Module 04 | Hands-on confidence with AI security events |
| Module 05 | Key Drivers for AI IR |
| Module 06 | Attack Surface |
| Module 07 | Models, training data, inference APIs |
| Module 08 | Evidence Types |
| Module 09 | AI System Architecture for IR |
| Module 10 | Key Components to Monitor |
| Module 11 | IR Touchpoints |
| Module 12 | AI Security Threat Landscape |
| Module 13 | Model-Level Threats |
| Module 14 | Data-Level Threats |
All hands-on labs run on Rocheston Rose X OS. Students practice ai incident response for beginners by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Explain Course Overview fundamentals
- Lab 2: Execute hands-on tasks for what you will learn
- Lab 3: Execute hands-on tasks for familiarity with general ir concepts a plus
- Lab 4: Execute hands-on tasks for hands-on confidence with ai security events
- Lab 5: Execute hands-on tasks for key drivers for ai ir
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for AI incident response for Beginners, 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