AI Application Security: Defending and Auditing AI Systems

As artificial intelligence becomes deeply integrated into modern software, securing these deployments is more critical than ever. Traditional security measures often fall short when protecting complex AI architectures from novel attack vectors. This written course equips you with the foundational knowledge to understand how AI applications and systems are targeted by attackers. You will transition from a beginner to someone who can recognize vulnerabilities in AI pipelines, analyze system-level risks, and apply essential defensive principles to secure your deployments. What you'll learn: - Understand foundational AI security concepts and the unique attack surfaces of machine learning systems - Identify common vulnerabilities in AI applications, including prompt injection, data poisoning, and model extraction - Analyze system-level security risks associated with the Model Context Protocol and external API integrations - Apply modern security frameworks like the OWASP Top 10 for Large Language Model Applications - Explore defensive strategies to sanitize inputs, validate outputs, and build resilient AI pipelines You will begin by exploring core terminology and fundamental security principles specific to AI. From there, the material guides you through real-world attack scenarios, system vulnerabilities, and practical mitigation strategies. This course is designed for software developers, system administrators, and security beginners who want to understand the modern threat landscape of artificial intelligence. No prior security experience is required. Begin reading today to build a safer, more resilient foundation for your AI integrations.