LLM application security Monitoring and Detection
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 monitoring course teaches comprehensive detection and observability strategies for proactive security operations. At an expert level, RCCE students will learn to instrument systems for security telemetry, build detection pipelines, configure alerting, and maintain monitoring coverage as environments evolve. Students gain the visibility and detection capabilities needed to catch threats early.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing LLM application security Monitoring and Detection
- Monitor and audit privilege usage; detect escalation attempts
- Execute hands-on tasks for learning objectives — covering AI Threat Modeling.
- Design a scalable privilege management architecture with policy and enforcement
- Build detections and response workflows for privilege escalation, including Establish AI governance.
- Explain LLM Application Architecture Overview fundamentals
- Execute hands-on tasks for user input
- Execute hands-on tasks for ▶ prompt engine
- Execute hands-on tasks for ▶ output filter
- Execute hands-on tasks for backend layer — covering Prompt templates & chains.
- Execute hands-on tasks for input surface
- Execute hands-on tasks for processing surface
| Module 01 | Monitoring and Detection |
| Module 02 | Learning Objectives |
| Module 03 | AI Threat Modeling |
| Module 04 | Governance & Response |
| Module 05 | LLM Application Architecture Overview |
| Module 06 | User Input |
| Module 07 | ▶ Prompt Engine |
| Module 08 | LLM Model |
| Module 09 | ▶ Output Filter |
| Module 10 | Backend Layer |
| Module 11 | Input Surface |
| Module 12 | Processing Surface |
| Module 13 | Output Surface |
| Module 14 | AI Threat Modeling Frameworks |
All hands-on labs run on Rocheston Rose X OS. Students practice llm application security monitoring and detection 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: Execute hands-on tasks for learning objectives
- Lab 3: Design a scalable privilege management architecture with policy and enforcement
- Lab 4: Build detections and response workflows for privilege escalation
- Lab 5: Explain LLM Application Architecture Overview fundamentals
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for LLM application security Monitoring and Detection, 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