Lilly Tech Systems
Learn AI Data Privacy
Master privacy regulations and compliance for machine learning systems. From GDPR and CCPA to data minimization and anonymization — build AI systems that respect user rights and meet legal requirements.
Your Learning Path
Follow these lessons in order to build a complete understanding of AI data privacy.
1. Introduction
Why data privacy matters for AI, the privacy landscape, training data rights, and the intersection of ML and personal data protection.
2. GDPR for AI
How the General Data Protection Regulation applies to machine learning, DPIAs, lawful basis for processing, and right to be forgotten.
3. CCPA/CPRA
California Consumer Privacy Act and its amendments. Opt-out rights, data broker obligations, and AI-specific provisions.
4. Data Minimization
Collect only what you need. Purpose limitation, storage limitation, data retention policies, and lean ML pipelines.
5. Anonymization
Techniques for de-identification, pseudonymization, k-anonymity, l-diversity, and t-closeness for ML training data.
6. Best Practices
Privacy-by-design for AI, compliance checklists, privacy impact assessments, and building a culture of data protection.
What You'll Learn
By the end of this course, you'll be able to:
Navigate Privacy Regulations
Understand GDPR, CCPA/CPRA, and other regulations as they apply to AI and ML systems.
Conduct DPIAs for AI
Perform Data Protection Impact Assessments specifically for machine learning projects.
Anonymize Training Data
Apply anonymization and pseudonymization techniques to protect personal data in ML pipelines.
Build Compliant AI
Design privacy-by-default AI systems that respect user rights and meet regulatory requirements.