Security Foundations for AI Software and Services

As artificial intelligence becomes central to modern software, securing these systems against unique vulnerabilities is more critical than ever. This course introduces you to the essential principles of protecting AI models, data pipelines, and software services from emerging threats. You will transition from understanding general software security to applying specialized defense strategies for AI-driven applications. By reading through our structured explanations and analyzing real-world scenarios, you will gain the confidence to identify risks, implement secure design principles, and safeguard AI integrations against malicious exploits. What you'll learn: Understand foundational AI security terminology, core concepts, and the unique threat landscape of machine learning; Identify common vulnerabilities in modern AI systems, including prompt injection, model poisoning, and data leakage; Apply secure coding practices and vulnerability assessment methodologies tailored to AI-driven applications; Learn how to secure data flows in Retrieval-Augmented Generation (RAG) pipelines and API integrations; Explore ethical considerations and industry-standard security frameworks to guide your development process. The course begins by establishing essential definitions and security baselines before guiding you through practical threat modeling, vulnerability mitigation, and secure deployment strategies for AI services. This course is designed for software developers, system architects, and tech enthusiasts who are new to AI security and want to build a solid foundation with no prior security experience required. Start reading today to build safer, more resilient AI-powered applications.