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Certified AI Data Protection Officer (CAIDPO)
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Learning Objectives
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Module 1: Foundations of AI-Driven Data Protection
Lesson 1.1 – Understanding AI, Privacy, and the Role of the AI DPO -
Lesson 1.2 – Global AI and Data Protection Regulations
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Worksheet: Global AI Data Law Tracker
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Module 2: Data Mapping, RoPAs and AI Model InventoriesLesson 2.1 – Mapping AI Data Flows and Building RoPAs for AI Models
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Lesson 2.2 – AI Model Documentation and Lifecycle Governance
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Template: AI RoPA
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Template: Model registry
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Module 3: AI-Specific DPIAs, Bias, and FairnessLesson 3.1 – Conducting AI-Specific Data Protection Impact Assessments (DPIAs)
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Lesson 3.2 – Fairness, Bias, and Discrimination in AI Systems
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Worksheet: AI DPIA
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Worksheet: Bias and fairness Heatmap
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Module 4: DSARs, AI Transparency and Consent ManagementLesson 4.1 – Handling DSARs and AI-Based Processing Requests
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Lesson 4.2 – Designing Consent and Transparency for AI Interfaces
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Template: DSAR AI Response
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Template: Consent UX Guide
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Module 5: AI Risk Governance, Audits and Ethical CultureLesson 5.1 – Building AI Risk Frameworks and Governance Committees
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Lesson 5.2 – Internal Audits, Training, and Compliance Culture for AI
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Template: AI Risk and Audit Tracker
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Template: Internal Policy Starter Kit
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Certified AI Data Protection Officer PlaybookAI Incident Report Form
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72-Hour Regulatory Breach Notification Checklist
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Stakeholder Communication Grid
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AI Vendor Risk Questionnaire
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Supplier Risk Scoring Sheet
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Contract Clause Tracker
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Explainability Summary Sheet (“Model Card”)
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Black-Box Risk Rating Worksheet
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Transparency Compliance Audit Checklist
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Human Oversight Decision Log
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Ethical AI Assessment Form
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Responsible AI Deployment Checklist
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AI Data Transfer Register
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AI Transfer Impact Assessment Worksheet
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Compliance Controls Matrix for AI Data Flows
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DPO Governance Scorecard
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AI Compliance Heatmap Worksheet
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Quarterly AI Governance Review Agenda
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AI Privacy Training Log
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AI Risk Register Template
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AI Audit Readiness Checklist
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Certified AI Data Protection Officer (CAIDPO)- Exam Guidance
Lesson 1 of 42
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Learning Objectives
August 17, 2025
By the end of this course, learners will be able to:
- Interpret and apply global data protection laws in the context of AI technologies and automated decision-making.
- Conduct privacy impact assessments and algorithmic audits for AI systems using compliant governance models.
- Build internal policies and compliance strategies for AI data usage, vendor selection, and risk reporting.
- Respond effectively to data subject rights requests (DSARs) related to AI-based data processing.
- Embed AI ethics, fairness, and explainability into privacy policies and product development workflows.