LLM & Generative AI Security: Protect AI Applications

“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 security controls can be used to reduce the associated risks. The course takes a deeper look at Prompt Injection, one of the most important attack categories affecting LLM applications. You will learn how attackers manipulate prompts and external content to influence model behavior, bypass intended restrictions, expose sensitive information, or interfere with downstream workflows.

You will also study jailbreaking, prompt chaining, prompt leakage, and indirect prompt injection, along with defensive strategies for reducing their impact. You will learn how to secure Retrieval-Augmented Generation (RAG) applications by examining the complete RAG architecture and its unique security challenges. Topics include retrieval attacks, document poisoning, vector database security, secure chunking, authorization, and access control.

You will understand why connecting an LLM to enterprise documents and knowledge bases introduces additional security boundaries and how organizations can design RAG systems that protect sensitive information. Modern AI systems are increasingly becoming autonomous through AI agents and tool calling. This course examines the security implications of agentic AI, including AI agent architecture, tool permissions, MCP security, least privilege, human approval workflows, and permission management.

You will learn why giving an AI system access to databases, APIs, files, applications, and external tools requires strong security controls and carefully designed authorization boundaries. You will also explore the security of AI APIs and integrations, including applications using technologies such as OpenAI, Claude, and Gemini. Topics include API authentication, authorization, secrets management, rate limiting, credential protection, and secure integration patterns.

These concepts will help you understand how to reduce the risks associated with exposing AI capabilities through APIs and connecting AI applications to external services. Defense is a major focus of the course. You will learn how to design and implement AI guardrails, including input filtering, output filtering, content moderation, prompt templates, policy engines, validation controls, and human-in-the-loop approval mechanisms.

You will also learn why no single guardrail can completely secure an AI system and how multiple defensive layers can be combined using a defense-in-depth approach. The course also introduces AI red teaming, giving you a structured approach for testing AI applications from an adversarial perspective. You will explore adversarial prompting, jailbreak testing, prompt fuzzing, attack automation, and red-team methodologies that security teams can use to identify weaknesses before attackers exploit them.

These techniques help bridge the gap between traditional penetration testing and the rapidly developing discipline of AI security testing. Security does not stop after deployment. You will learn how organizations can monitor AI systems in production using logging, security monitoring, detection rules, anomaly detection, alerting, audit trails, and SIEM integration.

You will understand which AI-related activities should be monitored and how security teams can identify suspicious behavior across prompts, model interactions, API activity, retrieval systems, and agent workflows. Finally, the course examines AI governance and the Secure AI Software Development Lifecycle (AI SDLC). You will explore frameworks and guidance including the NIST AI Risk Management Framework, OWASP guidance, and relevant ISO AI standards, while learning how organizations can establish AI policies, assess risk, and incorporate security throughout design, development, testing, deployment, monitoring, and continuous improvement.

This course is designed to combine AI security theory with practical, real-world security thinking. Whether you are a cybersecurity professional, SOC analyst, penetration tester, security engineer, developer, cloud engineer, AI/ML engineer, DevSecOps professional, IT professional, or technology student, you will gain a structured understanding of how modern AI applications can be attacked—and, more importantly, how they can be designed, tested, monitored, and governed more securely. By the end of the course, you will have a practical understanding of the rapidly evolving field of LLM and Generative AI security and the skills needed to evaluate security risks across LLMs, RAG applications, AI agents, vector databases, APIs, plugins, and enterprise AI systems.

You will be better prepared to participate in AI security assessments, secure AI development, AI red teaming, security architecture, AI governance, and the protection of production Generative AI applications.