GDPR & AI Data Privacy for Teams

Empower Your Team—Build AI Responsibly with Data Privacy by Design.

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

“GDPR & AI Data Privacy for Teams” is a targeted training program designed for cross-functional teams working with or deploying AI technologies. The course demystifies GDPR in the AI context—explaining how to collect, process, and store data responsibly, conduct risk assessments, manage consent, and design AI systems that comply with data protection laws. Whether you are in product, engineering, legal, marketing, or data science, this course offers a unified foundation for responsible AI deployment in line with European privacy standards.

Aim:

To equip professionals and teams with essential knowledge of the General Data Protection Regulation (GDPR) and its application in Artificial Intelligence (AI) systems, ensuring compliance, ethical usage, and responsible data handling throughout the AI lifecycle.

Program Objectives:

To build foundational GDPR literacy across AI-focused teams
To apply privacy and security principles in everyday AI development
To align organizational AI strategies with regulatory and ethical standards
To promote transparency, accountability, and trust in AI adoption

What you will learn?

Week 1: Foundations of Data Privacy and Regulation

Module 1: Understanding GDPR in the AI Context

Chapter 1.1: Introduction to GDPR – Principles and Scope
Chapter 1.2: Key Terms: Personal Data, Processing, Consent, Controller vs. Processor
Chapter 1.3: Relevance of GDPR for AI and ML Projects
Chapter 1.4: Territorial Scope and Extra-EU Impact

Module 2: Core Privacy Principles and Team Responsibilities

Chapter 2.1: Data Minimization, Purpose Limitation, and Storage Limitation
Chapter 2.2: Transparency, Fairness, and Accountability in AI Systems
Chapter 2.3: Privacy by Design and by Default
Chapter 2.4: Roles of Engineers, Analysts, Product Managers, and Legal Teams

Week 2: Working Safely with Data in AI Workflows

Module 3: Collecting, Storing, and Using Data

Chapter 3.1: Lawful Bases for Processing Data (Consent, Contract, Legitimate Interest)
Chapter 3.2: Anonymization vs. Pseudonymization in AI Projects
Chapter 3.3: Data Subject Rights (Access, Erasure, Portability, Objection)
Chapter 3.4: Using External Datasets and Data Sharing Agreements

Module 4: Risk Mitigation and Governance

Chapter 4.1: Data Protection Impact Assessments (DPIAs) for AI Use Cases
Chapter 4.2: Vendor Risk Management and Due Diligence
Chapter 4.3: Security Measures: Encryption, Access Controls, Logging
Chapter 4.4: Internal Audits and Record-Keeping for AI Models

Week 3: Compliance in Practice and Team Collaboration

Module 5: AI System Design and Auditability

Chapter 5.1: Building Traceable and Explainable AI Models
Chapter 5.2: Human-in-the-Loop and Review Mechanisms
Chapter 5.3: Consent Management and Logging User Interaction
Chapter 5.4: Aligning Model Deployment with Privacy Controls

Module 6: Preparing for Audits and Regulatory Expectations

Chapter 6.1: Documenting AI Workflows and Privacy Justifications
Chapter 6.2: Handling Data Breaches and Regulatory Reporting
Chapter 6.3: Case Studies: GDPR Violations in AI Products
Chapter 6.4: Building a Culture of Data Privacy in Cross-Functional Teams

Intended For :

Product managers, engineers, data scientists, legal & compliance professionals
Teams working on AI/ML, data platforms, analytics, or digital products
No prior legal background required