Responsible AI for Developers: Privacy and Safety Fundamentals

As artificial intelligence becomes deeply integrated into software systems, developers must prioritize user privacy and system security from day one. Building AI responsibly is no longer an afterthought—it is a core software engineering requirement. This text-based course guides you through the essential principles of responsible AI, focusing on practical privacy preservation and safety protocols. You will transition from writing basic AI integration code to designing secure, ethical, and compliant intelligent systems that protect user data and resist common vulnerabilities. What you'll learn: 1. Understand foundational AI ethics, safety principles, and privacy regulations. 2. Implement data anonymization and differential privacy techniques in training pipelines. 3. Mitigate modern security threats like prompt injection and data poisoning. 4. Configure alignment strategies to prevent harmful or biased model outputs. 5. Apply vulnerability testing and auditing practices to AI-powered applications. The course begins with core definitions of AI safety and privacy regulations, then transitions into practical defense strategies, data sanitization techniques, and modern vulnerability mitigation. This course is designed for software developers, data practitioners, and tech professionals who are new to AI safety and want to build secure systems. No prior background in security or advanced machine learning is required. Start reading today to build AI applications that users can trust.