Course Summary
This course focused on validating the technical skills and knowledge it takes to assess, build and implement a comprehensive privacy solutions. On this ISACA Credited Data Privacy Solutions Engineer (CDPSE) course, you’ll learn how to create privacy solutions and be responsible for your business’ privacy strategies to support its unimpeached growth.
You’ll learn the required technical skill-set of privacy-enhanced designs in order to build a common understanding of best practices throughout your organisation.
You’ll also learn to implement:
Privacy Impact Assessment (PIA)
Startegies against threats, attacks and vulnerabilities related to privacy – including encryption, hashing and de-identification
Data Inventory and classification (e.g., tagging, tracking, SOR)
Domain 1: Privacy Governance
Section A: Governance
Module 1: Personal Data and Information
Module 2: Privacy Laws and Standards across Jurisdictions
Module 3: Privacy Documentation (e.g., Policies, Guidelines)
Module 4: Legal Purpose, Consent, and Legitimate Interest
Module 5: Data Subject Rights
Section B: Management
Module 1: Roles and Responsibilities related to Data
Module 2: Privacy Training and Awareness
Module 3: Vendor and Third-Party Management
Module 4: Audit Process
Module 5: Privacy Incident Management
Section C: Risk Management
Module 1: Risk Management Process
Module 2: Privacy Impact Assessment (PIA)
Module 3: Threats, Attacks, and Vulnerabilities related to Privacy
Domain 2: Privacy Architecture
Section A: Infrastructure
Module 1: Technology Stacks
Module 2: Cloud-based Services
Module 3: Endpoints
Module 4: Remote Access
Module 5: System Hardening
Section B: Applications and Software
Module 1: Secure Development Lifecycle (e.g., Privacy by Design)
Module 2: Applications and Software Hardening
Module 3: APIs and Services
Module 4: Tracking Technologies
Section C: Technical Privacy Controls
Module 1: Communication and Transport Protocols
Module 2: Encryption, Hashing, and De-identification
Module 3: Key Management
Module 4: Monitoring and Logging
Module 5: Identity and Access Management
Domain 3: Data Cycle
Section A: Data Purpose
Module 1: Data Inventory and Classification (e.g., Tagging, Tracking, SOR)
Module 2: Data Quality and Accuracy
Module 3: Dataflow and Usage Diagrams
Module 4: Data Use Limitation
Module 5: Data Analytics (e.g., Aggregation, AI, Machine Learning, Big Data)
Section B: Data Persistence
Module 1: Data Minimisation (e.g., De-identification, Anonymisation)
Module 2: Data Migration
Module 3: Data Storage
Module 4: Data Warehousing (e.g., Data Lake)
Module 5: Data Retention and Archiving
Module 6: Data Destruction
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