Training Course on The Intersection of AI and Data Protection

Training Course on The Intersection of AI and Data Protection

Introduction

In today’s digital economy, artificial intelligence (AI) is transforming industries through automation, predictive analytics, and personalized experiences. However, as AI systems handle vast amounts of personal and sensitive data, they introduce significant privacy and data protection concerns. Training Course on The Intersection of AI and Data Protection explores the regulatory, technical, and ethical considerations organizations must address to ensure compliance and maintain trust in AI systems.

This training empowers participants to navigate global data protection laws like GDPR, CCPA, and emerging AI regulations while deploying AI responsibly. Through in-depth modules, real-world case studies, and interactive learning, participants will gain practical tools to mitigate AI risks, ensure lawful data processing, and align innovation with compliance.

Course Objectives

1. Understand AI governance and its relationship with data privacy laws.
2. Explore GDPR compliance in AI-driven data processing.
3. Identify and mitigate AI data security risks.
4. Evaluate automated decision-making under global data protection frameworks.
5. Apply privacy-by-design in AI system development.
6. Assess the impact of machine learning on individual privacy rights.
7. Interpret the role of data anonymization and differential privacy in AI.
8. Examine AI transparency and explainability techniques.
9. Navigate legal challenges of AI ethics and accountability.
10. Analyze real-time AI data breaches and incident response.
11. Comply with cross-border data transfer laws in AI contexts.
12. Develop robust AI risk assessment frameworks.
13. Master AI audit trails and documentation for regulatory compliance.

Target Audience

1. Data Protection Officers (DPOs)
2. Compliance Officers
3. AI Developers and Engineers
4. IT Security Professionals
5. Privacy Consultants
6. Corporate Legal Counsel
7. Risk Management Professionals
8. Policy Makers and Regulators

Course Duration: 5 days

Course Modules

Module 1: Introduction to AI and Data Protection

• Overview of AI and machine learning models
• Key concepts of personal data and data processing
• History and evolution of data protection laws
• The convergence of AI and privacy concerns
• Global regulatory frameworks (GDPR, CCPA, etc.)
• Case Study: Google DeepMind & NHS data-sharing controversy

Module 2: Legal & Ethical Frameworks

• Understanding GDPR articles relevant to AI
• Consent, legitimate interest, and transparency
• Automated decision-making and profiling
• Ethical implications of AI bias
• Compliance checklists and frameworks
• Case Study: Facial recognition and EU legal challenges

Module 3: Privacy by Design and Default in AI

• Embedding privacy in AI architecture
• Differential privacy and federated learning
• Data minimization and purpose limitation
• Privacy impact assessments (PIAs)
• Best practices for ethical AI development
• Case Study: Apple’s implementation of differential privacy

Module 4: AI Risk Management and Governance

• Identifying AI risks and vulnerabilities
• AI governance models and frameworks
• Risk mitigation strategies for AI systems
• Establishing accountability in AI projects
• Aligning business objectives with compliance
• Case Study: IBM Watson and risk mismanagement in healthcare

Module 5: Data Anonymization & Security in AI

• Techniques for anonymizing datasets
• Re-identification risks in AI analytics
• Secure data lifecycle management
• Encryption and data masking strategies
• Regulatory standards for AI data security
• Case Study: Netflix dataset re-identification incident

Module 6: AI Transparency and Explainability

• Importance of explainable AI (XAI)
• Building user trust through transparency
• Tools and methods for explainability
• Regulatory push for AI accountability
• Documenting AI decision-making processes
• Case Study: COMPAS algorithm in U.S. criminal justice

Module 7: Cross-Border Data Transfers and AI

• Overview of data transfer mechanisms (SCCs, BCRs)
• AI compliance in multinational operations
• Impact of Schrems II and data localization laws
• Role of cloud providers in data movement
• Risk assessment for international data flows
• Case Study: Meta’s data transfer suspension in Europe

Module 8: Building a Responsible AI Strategy

• Framework for ethical AI adoption
• Auditing AI systems for bias and compliance
• Stakeholder engagement and training
• Continuous monitoring and policy updates
• Integrating data protection into AI lifecycle
• Case Study: Microsoft’s Responsible AI initiative

Training Methodology

• Instructor-led virtual sessions with live Q&A
• Interactive workshops using real-world scenarios
• Hands-on case study analysis for experiential learning
• Quizzes and assessments for knowledge reinforcement
• Downloadable toolkits and compliance templates
• Peer collaboration forums for shared learning

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.