Responsible AI for Developers: Privacy and Safety Fundamentals

As artificial intelligence becomes central to modern software, developers must ensure their applications are safe, secure, and respectful of user privacy. Developing AI responsibly is no longer optional—it is a core engineering requirement. This course equips you with the foundational knowledge and practical strategies needed to build trustworthy AI systems. You will transition from writing basic AI integrations to designing robust, secure, and privacy-first applications that align with modern ethical standards and regulatory expectations. What you'll learn: • Understand foundational principles of responsible AI, including fairness, accountability, and transparency. • Implement privacy-preserving techniques to protect user data during model training and inference. • Identify and mitigate security risks such as prompt injection attacks and data poisoning. • Apply modern safety guardrails, including input filtering and output validation, to prevent harmful AI behavior. • Establish systematic testing and red teaming workflows to evaluate system safety before deployment. The journey begins with essential definitions of AI ethics and privacy regulations, before moving step-by-step into practical security architectures, threat modeling, and defensive coding patterns for AI applications. This course is designed for software developers, engineers, and technology enthusiasts who are new to AI safety and want to build secure applications. No prior background in machine learning or data science is required. Start reading today to build AI systems that your users can fully trust.