AI governance Incident Response
RCCE students will learn the governance, oversight, and management of artificial intelligence systems within organizations, covering AI risk assessment, ethical AI frameworks, model accountability, bias detection and mitigation, and AI regulatory compliance. RCCE students will learn to establish AI governance committees, define acceptable AI use policies, implement model risk management processes, conduct AI impact assessments, monitor AI system behavior for drift and unintended outcomes, comply with emerging AI regulations, and respond to incidents where AI systems produce harmful or unexpected results. This incident response course prepares students to act decisively during security incidents with structured workflows and clear decision frameworks. At an expert level, 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 AI governance Incident Response
- Execute hands-on tasks for advanced cyber defense mastery
- Explain Executive Overview fundamentals
- Execute hands-on tasks for course scope — covering AI systems introduce novel failure modes, AI risk assessment and ethical frameworks.
- Execute hands-on tasks for traditional ir playbooks insufficient for ai — covering AI risk assessment and ethical frameworks.
- Execute hands-on tasks for level: advanced — covering Architecture tradeoffs and automation patterns.
- Execute hands-on tasks for core definitions
- Design a scalable privilege management architecture with policy and enforcement
- Execute hands-on tasks for board / executive oversight layer
- Execute hands-on tasks for risk category
- Execute hands-on tasks for threat examples
- Execute hands-on tasks for impact level
| Module 01 | Advanced Cyber Defense Mastery |
| Module 02 | Executive Overview |
| Module 03 | Course Scope |
| Module 04 | Traditional IR playbooks insufficient for AI |
| Module 05 | Level: Advanced |
| Module 06 | Core Definitions |
| Module 07 | Model Accountability |
| Module 08 | AI Governance Framework Architecture |
| Module 09 | Board / Executive Oversight Layer |
| Module 10 | Risk Category |
| Module 11 | Threat Examples |
| Module 12 | Impact Level |
| Module 13 | Mitigation Approach |
| Module 14 | Model Integrity |
All hands-on labs run on Rocheston Rose X OS. Students practice ai governance incident response by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Execute hands-on tasks for advanced cyber defense mastery
- Lab 2: Explain Executive Overview fundamentals
- Lab 3: Execute hands-on tasks for course scope
- Lab 4: Execute hands-on tasks for traditional ir playbooks insufficient for ai
- Lab 5: Execute hands-on tasks for level: advanced
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for AI governance 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