Securing AI Systems: Safeguarding Machine Learning Applications

As artificial intelligence becomes central to modern software, securing these systems against specialized threats is more critical than ever. Traditional cybersecurity measures are no longer enough to protect complex machine learning models from sophisticated exploits. This written course guides you through the foundational concepts of AI security, helping you transition to an AI-safety-aware professional. You will learn to identify unique vulnerabilities in machine learning pipelines and implement robust defensive strategies to safeguard your data and models. What you'll learn: - Understand foundational AI security concepts and the unique threat landscape of machine learning. - Identify common vulnerabilities, including adversarial attacks, data poisoning, and model extraction. - Mitigate modern risks such as prompt injection and insecure output handling in large language models. - Secure Retrieval-Augmented Generation (RAG) pipelines and data storage components. - Apply practical defense mechanisms, input validation, and model guardrails to production systems. Starting with essential security definitions, the curriculum progresses systematically through data pipeline protection, model defense techniques, and modern security considerations for large language models. Through clear written explanations and code-based examples, you will build a solid framework for threat modeling in AI. This course is designed for software developers, aspiring security analysts, and tech enthusiasts who want to learn AI security from scratch. No prior background in machine learning or cryptography is required. Start reading today to build safer, more resilient intelligent systems.