Generative AI Cybersecurity: LLM, RAG & AI Agent Security

About this course

Generative AI Cybersecurity: LLM, RAG & AI Agent Security

Generative AI Cybersecurity: LLM, RAG & AI Agent Security is a practical course designed for cybersecurity professionals, security engineers, AI security professionals, application security teams, cloud security professionals, AI developers, security architects, red teamers, DevSecOps professionals and technical teams responsible for securing modern Generative AI applications.

The course explores how to secure Generative AI systems, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) applications, vector databases, AI agents, multi-agent systems, model interfaces, AI memory and tool-enabled AI workflows against emerging cybersecurity threats.

Learners explore modern LLM security, AI cybersecurity, OWASP threats for LLM applications, prompt injection, data exfiltration, hallucination risk, AI firewalls, AI Security Posture Management (AI-SPM), prompt-security testing, secure RAG architecture, vector poisoning, embedding attacks, LLM sandboxing, runtime controls, AI agent security, multi-agent security, red teaming, MLOps security and SecOps integration.

Rather than treating Generative AI security as an extension of traditional application security, the course examines the new attack surfaces introduced when AI systems can process natural-language instructions, retrieve external knowledge, maintain memory, call tools, interact with applications and coordinate with autonomous AI agents.

The objective is to help learners understand how modern Generative AI systems can be attacked, tested, monitored and hardened using practical security architectures, defensive controls and AI-specific security tooling.

Understanding the Generative AI Security Landscape

Generative AI introduces security challenges that differ from those found in traditional software applications.

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