Advanced AI data protection Mastery
RCCE students will learn protecting data within AI ecosystems including training data security, inference data privacy, model output controls, and AI-specific data governance. RCCE students will learn to classify and protect training datasets, implement data governance for AI pipelines, apply differential privacy and federated learning techniques, control access to model inference endpoints, prevent sensitive data leakage through model outputs, comply with AI-related data protection regulations, establish data retention and deletion policies for AI training data, and respond to incidents involving AI data exposure or unauthorized data use in model training. 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 data protection Mastery
- Execute hands-on tasks for ai data protection landscape — covering Scope of AI Data Risks.
- Execute hands-on tasks for training data classification & sensitivity
- Execute hands-on tasks for data sensitivity tiers — covering Public: open-source, licensed datasets.
- Execute hands-on tasks for classification methods — covering Automated scanning with DLP tools.
- Execute hands-on tasks for labeling and tagging best practices — covering Apply sensitivity labels before data enters the pipeline.
- Execute hands-on tasks for training data sensitivity decision matrix
- Execute hands-on tasks for data type
- Execute hands-on tasks for controls required
- Execute hands-on tasks for map, measure, manage, govern — covering Risk-tiered classification, AI management system standard.
- Execute hands-on tasks for ai data governance frameworks — covering NIST AI RMF.
- Execute hands-on tasks for ties to nist csf controls — covering Risk-tiered classification.
- Design a scalable privilege management architecture with policy and enforcement
| Module 01 | AI Data Protection Landscape |
| Module 02 | Training Data Classification & Sensitivity |
| Module 03 | Data Sensitivity Tiers |
| Module 04 | Classification Methods |
| Module 05 | Labeling and Tagging Best Practices |
| Module 06 | Training Data Sensitivity Decision Matrix |
| Module 07 | Data Type |
| Module 08 | Controls Required |
| Module 09 | Map, Measure, Manage, Govern |
| Module 10 | AI Data Governance Frameworks |
| Module 11 | Ties to NIST CSF controls |
| Module 12 | AI Data Governance Architecture |
| Module 13 | Data Owners |
| Module 14 | Governance Control Plane |
All hands-on labs run on Rocheston Rose X OS. Students practice advanced ai data protection mastery by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Execute hands-on tasks for ai data protection landscape
- Lab 2: Execute hands-on tasks for training data classification & sensitivity
- Lab 3: Execute hands-on tasks for data sensitivity tiers
- Lab 4: Execute hands-on tasks for classification methods
- Lab 5: Execute hands-on tasks for labeling and tagging best practices
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Advanced AI data protection 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