Foundations of AI Security: Defending Machine Learning Systems

As artificial intelligence and large language models integrate into modern applications, securing these systems requires a fundamental shift from traditional cybersecurity practices. Understanding how AI architectures differ from classic software is crucial to defending against novel vulnerabilities. This written course guides you through the core principles of AI security, helping you bridge the gap between classic information security and the unique demands of machine learning environments. You will learn to recognize vulnerabilities, analyze threat vectors, and implement defensive strategies to safeguard intelligent applications. What you'll learn: Understand the key differences between traditional cybersecurity and AI-specific threat landscapes; Analyze the three-layer AI architecture model to identify potential points of failure; Identify common AI vulnerabilities including jailbreaking, prompt injection, and data exfiltration; Explore modern security frameworks such as the OWASP Top 10 for Large Language Models; Apply secure design principles to Retrieval-Augmented Generation and model deployment; Formulate mitigation strategies to protect data privacy and maintain system integrity. The course begins with foundational definitions and architectural concepts before moving into real-world attack scenarios and practical, text-based defensive methodologies. You will progress from core terminology to analyzing complex security patterns through clear, written explanations and conceptual exercises. This course is designed for cybersecurity beginners, software developers, and IT professionals looking to understand the basics of AI security without requiring prior machine learning experience. Start reading today to build a strong foundation in securing the next generation of intelligent software.