Introduction to AI Security and Threat Mitigation

The rapid adoption of artificial intelligence introduces unprecedented security risks alongside its transformative benefits. From data poisoning to model exploitation, understanding how to protect AI-driven applications is now a critical skill for developers, IT professionals, and security enthusiasts alike. This text-based course equips you with the foundational knowledge required to identify, assess, and defend against security vulnerabilities in AI systems. You will transition from understanding basic security concepts to analyzing modern threats, enabling you to design and implement safer AI workflows. What you'll learn: - Understand core AI security concepts, terminology, and the unique attack surfaces of machine learning models. - Identify common vulnerabilities in large language models, including prompt injection and data leakage. - Apply basic threat modeling frameworks to assess risks in AI-driven applications and pipelines. - Secure training data and inference pipelines using privacy-preserving techniques and access controls. - Implement zero-trust principles and secure integration practices for third-party AI APIs. - Practice evaluating AI systems against emerging industry standards and safety benchmarks. The course begins with essential definitions and foundational concepts of artificial intelligence and information security. You will then progress through structured text lessons covering data protection, model vulnerabilities, practical mitigation strategies, and modern secure deployment practices. This course is designed for absolute beginners, IT professionals, and developers looking to understand AI security, with no prior experience in cybersecurity or machine learning required. Start reading today to build a secure foundation for the future of artificial intelligence.