LLM & Generative AI Security: Protect AI Applications

About this course

“This course contains the use of artificial intelligence”Artificial intelligence is rapidly transforming how applications are built, automated, and deployed—but it is also creating an entirely new generation of security risks. LLM & Generative AI Security Masterclass: Protect AI Applications is a comprehensive, practical course designed to help you understand, identify, and defend against the security threats affecting Large Language Models (LLMs), Generative AI applications, AI agents, Retrieval-Augmented Generation (RAG) systems, APIs, plugins, and AI-powered enterprise applications.

Throughout this course, you will develop a strong foundation in AI security by first understanding how modern LLM applications actually work. You will explore tokens, context windows, embeddings, transformers, vector databases, inference, fine-tuning, and AI application architectures. Understanding these components is essential because every layer of an AI system can introduce new attack surfaces, trust boundaries, and security risks.

You will then learn how to perform threat modeling for AI systems, identifying critical assets, data flows, trust boundaries, threat actors, and potential attack paths. You will explore how established security approaches such as STRIDE can be adapted to modern AI architectures and learn how to build practical threat models for LLM-powered applications.

A major portion of the course focuses on the security risks highlighted by the OWASP guidance for LLM and Generative AI applications. You will explore attacks such as direct and indirect prompt injection, prompt leakage, jailbreaking, sensitive information disclosure, insecure output handling, model poisoning, data poisoning, and AI supply-chain attacks. Rather than simply learning definitions, you will examine how these vulnerabilities can emerge in real AI applications and what securit

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