Zero-Trust Approach to Privacy by design
RCCE students will learn privacy-by-design methodology including data protection impact assessments, privacy architecture patterns, data minimization techniques, consent management, and privacy-enhancing technologies. RCCE students will learn to embed privacy requirements into system design from the earliest stages, conduct data protection impact assessments for new projects and systems, implement data minimization and purpose limitation principles, design consent collection and management workflows, apply privacy-enhancing technologies including anonymization, pseudonymization, and differential privacy, comply with GDPR, CCPA, and other privacy regulations, and build privacy review processes into development lifecycles. This zero-trust course applies modern security principles including least privilege, continuous verification, and explicit trust evaluation. Building on core knowledge, RCCE students will learn to implement zero-trust architectures that assume breach and verify every access request regardless of network location. Students build practical zero-trust implementations that align with organizational security modernization goals.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing Zero-Trust Approach to Privacy by design
- Design a scalable privilege management architecture with policy and enforcement
- Explain Privacy by Design: Seven Foundational Principles fundamentals
- Execute hands-on tasks for 4. full functionality: positive-sum
- Execute hands-on tasks for core tenets — covering Never trust, always verify, Assume breach at all times.
- Integrate privilege controls with identity providers and SIEM telemetry, including Apply zero-trust to data flows, and Verify every data access request.
- Execute hands-on tasks for data request
- Integrate privilege controls with identity providers and SIEM telemetry
- Execute hands-on tasks for access grant
- Execute hands-on tasks for trust evaluation criteria — covering User identity and role verification, Data classification level check.
- Execute hands-on tasks for enforcement points — covering API gateway policy enforcement, Data access proxy layer.
| Module 01 | Zero-Trust Approach to Privacy by Design |
| Module 02 | Privacy by Design: Seven Foundational Principles |
| Module 03 | 4. Full Functionality: Positive-Sum |
| Module 04 | Zero-Trust Architecture Fundamentals |
| Module 05 | Core Tenets |
| Module 06 | Privacy Integration |
| Module 07 | Zero-Trust Privacy Model |
| Module 08 | Data Request |
| Module 09 | → Identity Verify → Purpose Check → Consent Verify → |
| Module 10 | Access Grant |
| Module 11 | Trust Evaluation Criteria |
| Module 12 | Enforcement Points |
| Module 13 | Zero-Trust Classification Rules |
| Module 14 | All data defaults to Restricted until classified |
All hands-on labs run on Rocheston Rose X OS. Students practice zero-trust approach to privacy by design by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Design a scalable privilege management architecture with policy and enforcement
- Lab 2: Explain Privacy by Design: Seven Foundational Principles fundamentals
- Lab 3: Execute hands-on tasks for 4. full functionality: positive-sum
- Lab 4: Design a scalable privilege management architecture with policy and enforcement
- Lab 5: Execute hands-on tasks for core tenets
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Zero-Trust Approach to Privacy by design, 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