AI data protection Operations Playbook
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 operations-focused course delivers production-ready playbooks, checklists, and standard operating procedures. At an expert level, RCCE students will learn to build repeatable day-to-day operational workflows that ensure consistency and quality. Students receive templates and frameworks they can customize and deploy immediately in their security operations, reducing time to operational effectiveness.
- 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 data protection Operations Playbook
- Execute hands-on tasks for operations playbook
- Explain Course Overview & Learning Objectives fundamentals
- Execute hands-on tasks for course focus
- Execute hands-on tasks for key outcomes — covering Protect data across AI lifecycles.
- Design a scalable privilege management architecture with policy and enforcement
- Execute hands-on tasks for data sources — covering Processing Layer, Model Layer.
- Explain Training Data Security Foundations fundamentals
- Execute hands-on tasks for data provenance tracking — covering Document origin of every dataset, Maintain chain-of-custody logs.
- Execute hands-on tasks for access control matrix — covering Role-based dataset access, Environment isolation (dev/staging/prod).
- Execute hands-on tasks for integrity verification — covering Cryptographic hashing at ingestion, Checksums on transformation outputs.
- Execute hands-on tasks for encryption standards — covering AES-256 at rest for all training data, TLS 1.3 in transit between pipeline stages.
- Execute hands-on tasks for data provenance & lineage tracking
| Module 01 | Operations Playbook |
| Module 02 | Course Overview & Learning Objectives |
| Module 03 | Course Focus |
| Module 04 | Key Outcomes |
| Module 05 | Data Collection → Data Processing → Model Training → |
| Module 06 | Data Sources |
| Module 07 | Training Data Security Foundations |
| Module 08 | Data Provenance Tracking |
| Module 09 | Access Control Matrix |
| Module 10 | Integrity Verification |
| Module 11 | Encryption Standards |
| Module 12 | Data Provenance & Lineage Tracking |
| Module 13 | Data Lineage Chain |
| Module 14 | Data Governance Framework for AI Pipelines |
All hands-on labs run on Rocheston Rose X OS. Students practice ai data protection operations playbook by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Execute hands-on tasks for operations playbook
- Lab 2: Explain Course Overview & Learning Objectives fundamentals
- Lab 3: Execute hands-on tasks for course focus
- Lab 4: Execute hands-on tasks for key outcomes
- Lab 5: Design a scalable privilege management architecture with policy and enforcement
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for AI data protection Operations Playbook, 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