Certified Data Privacy Solutions Engineer (CDPSE)

The Certified Data Privacy Solutions Engineer (CDPSE) course is a comprehensive program designed to equip learners with the necessary skills to implement privacy solutions and manage data protection within their organizations effectively.


Learning Objectives and Outcomes:

  • Build and Implement Privacy Solutions
  • Manage the data lifecycle and advise technologists on privacy compliance
  • Implement privacy by design which results in privacy technology platforms and products that build trust and advance data privacy.
  • Ensure that privacy solutions match the organization’s risk appetite and mitigate risks of noncompliance
  • Improve the end user experience while preserving privacy and retaining trust.

To ensure a successful learning experience in the Certified Data Privacy Solutions Engineer (CDPSE) course, students should meet the following minimum prerequisites:

  • A basic understanding of IT concepts and terminology, especially as they relate to data handling and information systems.
  • Familiarity with general principles of data protection and privacy, including awareness of personal data and its sensitivity.
  • Knowledge of key privacy laws and regulations that pertain to data protection, such as GDPR, CCPA, or other regional privacy frameworks.
  • Some exposure to information security principles and practices, including an understanding of risk management processes.
  • An awareness of the importance of data subject rights and the general legal requirements for handling personal data.
  • For the technical aspects of the course, a foundational understanding of information technology systems, including cloud-based services, endpoints, and secure development practices.
  • Critical thinking and analytical skills to understand and apply privacy governance concepts in a variety of practical scenarios.

The Certified Data Privacy Solutions Engineer (CDPSE) course equips professionals with essential skills for privacy governance, architecture, and data lifecycle management.

  • Data Privacy Officers
  • Compliance Officers and Lawyers specializing in data privacy
  • Information Security Analysts
  • IT Managers and Consultants
  • Risk Assessment Professionals
  • Data Protection Managers
  • Cybersecurity Professionals
  • Systems and Network Administrators
  • Software Developers with a focus on privacy
  • Cloud Security Specialists
  • Data Governance and Quality Managers
  • Privacy and Security Architects
  • IT Auditors involved in privacy audits
  • Corporate Training Professionals specializing in privacy and compliance
  • Government Officials dealing with data protection regulations
  • HR Professionals overseeing employee data privacy
  • Marketing Managers who handle customer data
  • Product Managers incorporating privacy into product design
  • Business Analysts involved in data-sensitive projects
Section A: Governance
Personal Data and Information
Privacy Laws and Standards across Jurisdictions
Privacy Documentation (e.g., Policies, Guidelines)
Legal Purpose, Consent, and Legitimate Interest
Data Subject Rights

Section B: Management
Roles and Responsibilities related to Data
Privacy Training and Awareness
Vendor and Third-Party Management
Audit Process
Privacy Incident Management

Section C: Risk Management
Risk Management Process
Privacy Impact Assessment (PIA)
Threats, Attacks, and Vulnerabilities related to Privacy
Section A: Infrastructure
Technology Stacks
Cloud-based Services
Remote Access
System Hardening

Section B: Applications and Software
Secure Development Lifecycle (e.g., Privacy by Design)
Applications and Software Hardening
APIs and Services
Tracking Technologies

Section C: Technical Privacy Controls
Communication and Transport Protocols
Encryption, Hashing, and De-identification
Key Management
Monitoring and Logging
Identity and Access Management
Section A: Data Purpose
Data Inventory and Classification (e.g., Tagging, Tracking, SOR)
Data Quality and Accuracy
Dataflow and Usage Diagrams
Data Use Limitation
Data Analytics (e.g., Aggregation, AI, Machine Learning, Big Data)

Section B: Data Persistence
Data Minimisation (e.g., De-identification, Anonymisation)
Data Migration
Data Storage
Data Warehousing (e.g., Data Lake)
Data Retention and Archiving
Data Destruction
Length of exam 4 hours
Number of questions 150  questions
Question format Multiple choice
Passing grade 450 points out of 800 points
Testing center PSI Testing Center