Advanced AI monitoring Mastery
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 advanced mastery course challenges experienced practitioners with complex scenarios, expert-level techniques, and nuanced decision-making. At an expert level, RCCE students will learn to handle the most demanding situations in this domain, developing the expertise expected of senior security professionals. Students tackle multi-layered problems that require synthesizing knowledge across multiple disciplines.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing Advanced AI monitoring Mastery
- Monitor and audit privilege usage; detect escalation attempts
- Explain Course Overview & Objectives fundamentals
- Execute hands-on tasks for what you will master — covering AI threat modeling & risk assessment, Prompt injection attack/defense techniques.
- Execute hands-on tasks for who this course is for — covering Senior security engineers & architects, AI/ML operations professionals.
- Execute hands-on tasks for course structure — covering 7 foundation modules with hands-on labs, Attack simulation + defense exercises.
- Execute hands-on tasks for the ai security landscape
- Execute hands-on tasks for security gaps
- Execute hands-on tasks for regulatory pressure — covering Model supply chain vulnerabilities.
- Execute hands-on tasks for shadow ai & ungoverned deployments — covering EU AI Act enforcement timelines.
- Explain AI Threat Modeling Foundations fundamentals
- Execute hands-on tasks for stride for ai systems — covering Spoofing: model impersonation attacks.
- Execute hands-on tasks for ai attack surface areas — covering Training pipeline & data ingestion.
| Module 01 | Advanced AI Monitoring Mastery |
| Module 02 | Course Overview & Objectives |
| Module 03 | What You Will Master |
| Module 04 | Who This Course Is For |
| Module 05 | Course Structure |
| Module 06 | The AI Security Landscape |
| Module 07 | Security Gaps |
| Module 08 | Regulatory Pressure |
| Module 09 | Shadow AI & ungoverned deployments |
| Module 10 | AI Threat Modeling Foundations |
| Module 11 | STRIDE for AI Systems |
| Module 12 | AI Attack Surface Areas |
| Module 13 | MAESTRO Framework for AI Threat Modeling |
| Module 14 | Key Outputs |
All hands-on labs run on Rocheston Rose X OS. Students practice advanced ai monitoring mastery 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: Explain Course Overview & Objectives fundamentals
- Lab 3: Execute hands-on tasks for what you will master
- Lab 4: Execute hands-on tasks for who this course is for
- Lab 5: Execute hands-on tasks for course structure
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Advanced AI monitoring Mastery, 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