Generative Ai Data Privacy And Safe Use For Developers By Rupesh Tiwari
 

Developers building generative AI features often introduce privacy risks, security vulnerabilities, and compliance gaps without realizing it.
What you'll learn
Developers building generative AI features often introduce privacy risks, security vulnerabilities, and compliance gaps without realizing it. In this course, Generative AI Data Privacy and Safe Use for Developers, you'll gain the ability to build generative AI applications that are secure, privacy-preserving, and governance-ready. First, you'll explore how data flows through a generative AI system and where privacy risks emerge at each trust boundary, including prompt leakage, data retention, and unintended memorization. Next, you'll discover the OWASP Top 10 security threats for LLM applications and learn to implement layered safeguards including input validation, retrieval rails, output filtering, and evaluation gates. Finally, you'll learn how to apply responsible AI principles and implement governance controls that produce auditable evidence for frameworks like the EU AI Act and NIST AI RMF. When you're finished with this course, you'll have the skills and knowledge of generative AI data privacy and safe use needed to build production applications that protect user data, resist attacks, pass evaluation gates, and support compliance readiness for regulatory frameworks.
Homepage