Certified Data Privacy Solutions Engineer (CDPSE)
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ISACA’s new Certified Data Privacy Solutions Engineer (CDPSE) certification is an experience-based, technical certification: the first of its kind. CDPSE assesses a technology professional’s ability to implement privacy by design to enable organizations to enhance privacy technology platforms and products that provide benefits to consumers, build trust and advance data privacy.
Additionally, ISACA’s new Certified Data Privacy Solutions Engineer (CDPSE) certification not only focuses on privacy governance, it addresses technical privacy controls and how to design and embed them within an organization’s infrastructure. This certification is intended to bridge the gap between the technical and legal functions within an organization.
Course Syllabus
Module
Domain 1: Privacy Governance
A. Governance
1. Personal Data and Information
2. Privacy Laws and Standards across Jurisdictions
3. Privacy Documentation (e.g., Policies, Guidelines)
4. Legal Purpose, Consent, and Legitimate Interest
5. Data Subject Rights
B. Management
1. Roles and Responsibilities related to Data
2. Privacy Training and Awareness
3. Vendor and Third-Party Management
4. Audit Process
5. Privacy Incident Management
C. Risk Management
1. Risk Management Process
2. Privacy Impact Assessment (PIA)
3. Threats, Attacks, and Vulnerabilities related to Privacy
Domain 2: Privacy Architecture
A. Infrastructure
1. Technology Stacks
2. Cloud-based Services
3. Endpoints
4. Remote Access
5. System Hardening
B. Applications and Software
1. Secure Development Lifecycle (e.g., Privacy by Design)
2. Applications and Software Hardening
3. APIs and Services
4. Tracking Technologies
C. Technical Privacy Controls
1. Communication and Transport Protocols
2. Encryption, Hashing, and De-identification
3. Key Management
4. Monitoring and Logging
5. Identity and Access Management
Domain 3: Data Cycle
A. Data Purpose
1. Data Inventory and Classification (e.g., Tagging, Tracking, SOR)
2. Data Quality and Accuracy
3. Dataflow and Usage Diagrams
4. Data Use Limitation
5. Data Analytics (e.g., Aggregation, AI, Machine Learning, Big Data)
B. Data Persistence
1. Data Minimization (e.g., De-identification, Anonymization)
2. Data Migration
3. Data Storage
4. Data Warehousing (e.g., Data Lake)
5. Data Retention and Archiving
6. Data Destruction
