Securing AI Systems: Data Safety and Privacy Fundamentals

As artificial intelligence becomes central to modern software, securing the data that powers these systems is more critical than ever. Traditional cybersecurity measures are no longer enough to protect against unique AI vulnerabilities like data poisoning and model exploitation. This text-based course guides you through the essential concepts of AI data safety, privacy regulations, and defensive practices. You will transition from understanding basic security concepts to confidently identifying vulnerabilities and implementing modern, responsible AI standards in your projects. What you'll learn: - Understand foundational terminology in AI safety, data privacy, and secure model development - Identify modern threats unique to AI, including prompt injection, data poisoning, and model inversion - Apply privacy-preserving techniques such as differential privacy and data anonymization - Navigate key compliance frameworks and global regulations governing AI data usage - Implement zero-trust principles and secure access controls for vector databases and training pipelines - Practice threat modeling through written scenarios to design resilient AI workflows Starting with core definitions and the modern threat landscape, the curriculum moves step-by-step into practical defense strategies and regulatory compliance. You will reinforce your learning through written case studies and conceptual exercises designed to simulate real-world security challenges. This course is designed for beginners, developers, and data professionals looking to transition into AI security. No prior background in cybersecurity or advanced machine learning is required. Equip yourself with the knowledge to build safer, more trustworthy AI systems today.