AI monitoring Incident Handling: Blueprint
RCCE students will learn AI threat modeling, prompt injection defenses, model security, AI data protection, and responsible AI deployment. RCCE students will learn to secure AI systems throughout their lifecycle, protect training data and model integrity, detect adversarial attacks against machine learning systems, and establish governance frameworks for safe AI operations. 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 AI monitoring Incident Handling: Blueprint
- Monitor and audit privilege usage; detect escalation attempts
- Build detections and response workflows for privilege escalation, including AI-specific attack surfaces.
- Execute hands-on tasks for why it matters — covering Protecting AI models from manipulation, AI powers critical infrastructure decisions.
- Execute hands-on tasks for guarding against misuse of ai capabilities — covering AI powers critical infrastructure decisions.
- Explain AI Attack Surface Overview fundamentals
- Execute hands-on tasks for training pipeline — covering Data.
- Design a scalable privilege management architecture with policy and enforcement, including Model theft &.
- Execute hands-on tasks for inference api — covering Prompt.
- Execute hands-on tasks for deployment env — covering Container.
- Design a scalable privilege management architecture with policy and enforcement, including Spoofing: forged model identities, and Adversarial Threat Landscape for AI.
- Execute hands-on tasks for stride for ai — covering Spoofing: forged model identities.
- Execute hands-on tasks for mitre atlas — covering Adversarial Threat Landscape for AI.
| Module 01 | AI Monitoring Incident Handling: |
| Module 02 | Execute AI incident response |
| Module 03 | Why It Matters |
| Module 04 | Guarding against misuse of AI capabilities |
| Module 05 | AI Attack Surface Overview |
| Module 06 | Training Pipeline |
| Module 07 | Model Layer |
| Module 08 | Inference API |
| Module 09 | Deployment Env |
| Module 10 | AI Threat Modeling Frameworks |
| Module 11 | STRIDE for AI |
| Module 12 | MITRE ATLAS |
| Module 13 | Training Phase |
| Module 14 | Production Phase |
All hands-on labs run on Rocheston Rose X OS. Students practice ai monitoring incident handling: blueprint by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Monitor and audit privilege usage; detect escalation attempts
- Lab 2: Build detections and response workflows for privilege escalation
- Lab 3: Execute hands-on tasks for why it matters
- Lab 4: Execute hands-on tasks for guarding against misuse of ai capabilities
- Lab 5: Explain AI Attack Surface Overview fundamentals
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for AI monitoring Incident Handling: Blueprint, 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