AI governance Threats and Detection
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 threat-focused course teaches students to think like adversaries while building robust defenses. Building on core knowledge, RCCE students will learn to analyze attack techniques, build detection logic, and implement defensive strategies that proactively identify threats before they cause damage. Students develop a threat-informed mindset that drives better security decisions across all operational activities.
- 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 Threats and Detection
- Build detections and response workflows for privilege escalation
- Execute hands-on tasks for advanced cyber defense mastery
- Explain Executive Overview fundamentals — covering AI systems drive critical decisions across.
- Execute hands-on tasks for why ai governance matters — covering AI systems drive critical decisions across.
- Execute hands-on tasks for regulatory pressure
- Execute hands-on tasks for ethical obligation
- Execute hands-on tasks for threat surface
- Execute hands-on tasks for business risk
- Execute hands-on tasks for core definitions
- Design a scalable privilege management architecture with policy and enforcement
| Module 01 | AI Governance Threats and Detection |
| Module 02 | Advanced Cyber Defense Mastery |
| Module 03 | Executive Overview |
| Module 04 | Why AI Governance Matters |
| Module 05 | Regulatory Pressure |
| Module 06 | Ethical Obligation |
| Module 07 | Threat Surface |
| Module 08 | Business Risk |
| Module 09 | Core Definitions |
| Module 10 | Model Accountability |
| Module 11 | Bias Detection |
| Module 12 | AI Governance Framework Architecture |
| Module 13 | Strategic Layer |
| Module 14 | ↓ Directives & Policies |
All hands-on labs run on Rocheston Rose X OS. Students practice ai governance threats and detection by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Build detections and response workflows for privilege escalation
- Lab 2: Execute hands-on tasks for advanced cyber defense mastery
- Lab 3: Explain Executive Overview fundamentals
- Lab 4: Execute hands-on tasks for why ai governance matters
- Lab 5: Execute hands-on tasks for regulatory pressure
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for AI governance Threats 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