AI Security Fundamentals: Threats, Vulnerabilities, and Defenses

As artificial intelligence becomes deeply integrated into modern software, securing these systems against specialized cyber threats is more critical than ever. Traditional security measures are no longer sufficient to protect machine learning models and data pipelines from sophisticated exploits. This comprehensive text-based course equips you with the fundamental knowledge to identify, analyze, and defend against AI-specific security vulnerabilities. You will learn to: Understand the core terminology and architecture of AI systems from a security perspective; Identify modern AI threats including prompt injection, data poisoning, and model evasion; Apply secure design principles and zero-trust concepts to machine learning workflows; Implement defensive strategies such as input sanitization and adversarial robustness testing; Analyze real-world scenarios of AI breaches to prevent future security failures. You will start with foundational AI and security definitions before exploring practical threat modeling, vulnerability detection, and defense implementation. This course is designed for IT professionals, developers, and cybersecurity beginners who want to secure AI applications; no prior background in machine learning is required. Begin reading today to build a strong foundation in protecting intelligent systems from modern cyber threats.