Practical Data Privacy for AI Systems

As AI becomes more integrated into products and services, handling data responsibly is no longer optional—it's a requirement. This course provides a clear framework for understanding and implementing data privacy principles within your AI projects. You will move from basic awareness to confidently applying techniques that safeguard personal information, mitigate risks, and ensure your AI systems are both effective and ethical. What you'll learn: - Understand the core principles of modern data privacy regulations and how they apply to AI. - Apply practical techniques like anonymization, pseudonymization, and data masking to protect sensitive information. - Identify AI-specific privacy risks, including data leakage and model inference attacks. - Learn the foundational concepts of Privacy-Enhancing Technologies (PETs) like federated learning. - Develop a structured approach to assessing privacy impacts in your AI projects. - Integrate ethical considerations and fairness into your AI development workflow. The course begins with the essential terminology of data privacy and AI ethics, building a solid foundation before moving into practical methods for secure data handling and risk assessment in AI systems. This course is designed for beginners. No prior experience in law, cybersecurity, or advanced AI is required. It is ideal for developers, data analysts, product managers, and anyone looking to build responsible AI solutions. Start learning how to build trustworthy AI today.