Data Privacy and Security Training for AI Systems 

Best Practices for Data Protection in the AI Era
Why This Training?
In our rapidly evolving digital landscape, data has become the new gold. But with its immense value come serious responsibilities. AI systems, while revolutionary, pose unique challenges and vulnerabilities when it comes to data privacy and security. With the EU AI Act laying down strict mandates for data protection within AI, it’s crucial for organizations to be ahead of the curve. Our training equips you to:
  • Understand the intersection of AI and data privacy.
  • Navigate complex data protection regulations effectively.
  • Implement robust security measures tailored to AI systems.
Duration: 1 Day (8 hours), (online / virtual live session)

Who Should Attend?

 AI Practitioners & Engineers: Understand the nuances of handling data in AI projects.
 Data Privacy Officers & Legal Teams: Get insights on the regulatory landscape surrounding AI and data.
See more  
 Business Leaders & Decision Makers: Learn the implications of data breaches and the importance of compliance.
 IT Security Professionals: Upgrade your skills with AI-specific data security practices.

Course Highlights

 Comprehensive sessions on the foundations of data privacy in AI.
 Deep dive into the EU AI Act and its mandates on data protection.
 Hands-on workshop simulating AI data challenges.
 Expert-led discussions on the future of data privacy in AI.


 Basic understanding of data protection principles.
 Familiarity with AI concepts (not mandatory, but will enhance understanding).
 An open mind and willingness to engage in collaborative discussions.

Training Materials Needed by Participants

Laptop or tablet with internet connectivity.
Pre-installed video conferencing tool (link will be provided before training).
Digital or physical notebook for notes.
Recommended reading (a list will be sent prior to training).
Write your awesome label here.

Training Content

Data Privacy and Security Training for AI Systems 

Objective: Empower teams with robust knowledge and skills in data privacy and security, specifically in the context of AI. Align practices with the EU AI Act's mandates to ensure compliance, enhance privacy protection, and bolster AI implementations.

Session 1: The Foundations of Data Privacy and Security in AI

  • Introduction to Data Privacy: What it is and why it matters in AI.
  • The EU AI Act: Understanding its mandates on data privacy and security.
  • The Convergence of AI and Data Privacy: The unique challenges and opportunities.

Session 2: Recognizing and Handling Sensitive Data in AI

  • Types of Sensitive Data: Personal data, PII, biometric data, and more.
  • Challenges in AI: Data biases, skewed datasets, and the potential for misuse.
  • Activity: Identifying and classifying different types of data.

Session 3: Key Data Protection Principles in AI

  • Data Minimization: Collecting only necessary data.
  • Data Anonymization: Techniques and their importance in AI.
  • Purpose Limitation: Ensuring data is used only for its intended purpose.

Session 4: Security Measures for AI Data

  • Encryption and Tokenization: Securing data at rest and in transit.
  • Secure Data Storage: Best practices for storing AI data.
  • Monitoring and Auditing: Tracking data access and ensuring integrity.

Session 5: GDPR, CCPA, and the EU AI Act: A Comparative Look

  • Overview of Major Data Protection Regulations.
  • Specific Requirements of the EU AI Act related to data privacy.
  • Case Study: A breach scenario and its implications under different regulations.

Session 6: Privacy by Design in AI Systems

  • What is Privacy by Design? An introduction.
  • Implementing Privacy by Design in AI: Steps and best practices.
  • Activity: Designing a sample AI system with privacy considerations.

Session 7: Hands-on Workshop: Securing AI Data

  • Group Activity: Simulating a data security challenge in AI.
  • Workshop: Implementing encryption and anonymization techniques.
  • Feedback and Discussions: Sharing experiences and learnings.

Session 8: The Future of Data Privacy in AI

  • Evolving Landscape: Potential changes to data privacy regulations.
  • AI Innovations in Data Privacy: Techniques like differential privacy.
  • Open Floor Q&A: Addressing questions, sharing resources, and looking ahead.

Session 9: Wrapping Up and Actionable Takeaways

  • Recap of the Day: Key lessons and insights.
  • Action Points: Steps organizations can take immediately.
  • Networking and Further Collaboration: Encouraging peer learning and exchange.
Created with