AI Security Fundamentals: Safeguarding Models and Data

As artificial intelligence becomes deeply integrated into modern software, securing these systems against unique vulnerabilities has become a critical priority. Traditional security measures are no longer enough to protect intelligent applications from sophisticated, AI-specific threats. This written course guides you through the essential concepts of AI security, helping you transition from a general technology enthusiast to a practitioner who understands how to defend machine learning models. You will explore how AI systems fail, how attackers exploit them, and how to design robust defenses for real-world deployments. What you'll learn: 1. Understand foundational AI security concepts, terminology, and the unique attack surface of machine learning pipelines. 2. Identify common vulnerabilities in Large Language Models, including prompt injection and data poisoning. 3. Analyze model extraction and evasion attacks to protect proprietary algorithms and intellectual property. 4. Apply secure data handling practices to ensure privacy and prevent sensitive training data leakage. 5. Evaluate modern governance frameworks and secure deployment strategies for AI-driven applications. You will begin by learning core AI terminology and foundational security definitions before moving on to practical threat modeling and defensive tactics. The written lessons walk you through conceptual attack scenarios and step-by-step mitigation strategies to build your security mindset. This course is designed for software developers, IT professionals, and security beginners who want to build a solid foundation in AI safety without needing an advanced mathematical background. Start reading today to build the skills needed to protect the next generation of intelligent software.