AI Security Foundations: Defending Machine Learning and LLMs

As artificial intelligence becomes deeply integrated into modern software, securing these systems against unique vulnerabilities is more critical than ever. This course guides you through the essential principles of securing machine learning models and generative AI applications from real-world threats. By completing this course, you will transition from understanding basic security concepts to identifying, mitigating, and preventing specialized AI vulnerabilities. You will gain the confidence to analyze model pipelines, protect against adversarial attacks, and implement secure AI governance frameworks within any organization. What you'll learn: - Understand foundational AI security terminology, including the core differences between traditional cybersecurity and machine learning security. - Identify and defend against common vulnerabilities like prompt injection, data poisoning, and model evasion. - Apply industry-standard frameworks, including the OWASP Top 10 for Large Language Models, to evaluate system risks. - Configure secure deployment pipelines for machine learning models to prevent unauthorized access and data leakage. - Implement AI governance and compliance practices to ensure ethical, transparent, and secure model operations. The curriculum begins with fundamental AI definitions and security concepts before progressing to the analysis of adversarial threats, practical defense strategies, and modern governance frameworks. This text-based course is designed for cybersecurity beginners, developers, and IT professionals looking to build a strong foundation in AI security without any prior machine learning experience. Start reading today to build the skills needed to protect the future of artificial intelligence.