Practical AI incident response Workshop
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. This practice-intensive course emphasizes applied skills through lab exercises, real-world scenarios, and production-realistic workflows. Starting from foundational concepts, RCCE students will learn by doing, building muscle memory and practical confidence through repeated hands-on engagement. Students complete exercises that mirror actual workplace tasks, ensuring skills transfer directly to their professional 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 Practical AI incident response Workshop
- Explain Course Overview fundamentals
- Execute hands-on tasks for learning path
- Execute hands-on tasks for who this is for — covering Hands-On Focus, Lab exercises in every module.
- Execute hands-on tasks for hands-on focus — covering Lab exercises in every module.
- Execute hands-on tasks for why it matters — covering Structured process for AI security events, AI systems are high-value targets.
- Execute hands-on tasks for traditional ir vs ai ir — covering Traditional IR focuses on network/host/application layers.
- Explain AI System Architecture Overview fundamentals
- Execute hands-on tasks for core components — covering Training data pipelines and storage.
- Execute hands-on tasks for security touchpoints — covering Data validation and sanitization.
- Design a scalable privilege management architecture with policy and enforcement
- Execute hands-on tasks for data breaches — covering Unauthorized model modification, Training data exfiltration.
- Design a scalable privilege management architecture with policy and enforcement, including Adversarial input attacks.
| Module 01 | Course Overview |
| Module 02 | Learning Path |
| Module 03 | Who This Is For |
| Module 04 | Hands-On Focus |
| Module 05 | Why It Matters |
| Module 06 | Traditional IR vs AI IR |
| Module 07 | AI System Architecture Overview |
| Module 08 | Core Components |
| Module 09 | Security Touchpoints |
| Module 10 | Model Compromise |
| Module 11 | Data Breaches |
| Module 12 | Model Manipulation |
| Module 13 | Output Abuse |
| Module 14 | Response Time |
All hands-on labs run on Rocheston Rose X OS. Students practice practical ai incident response workshop 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 learning path
- Lab 3: Execute hands-on tasks for who this is for
- Lab 4: Execute hands-on tasks for hands-on focus
- Lab 5: Execute hands-on tasks for why it matters
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Practical AI incident response Workshop, 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