AI Agents and Agentic RAG for Cybersecurity
AI Agents and Agentic RAG for Cybersecurity
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 5h 20m | 1.21 GB
Instructor: Omar Santos
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 5h 20m | 1.21 GB
Instructor: Omar Santos
Overview
A hands-on approach to RAG, Langchain, LangGraph, and LlamaIndex and AI applications.
This course will teach critical skills in AI-driven cybersecurity and network optimization. The following core skills are covered:
- Master the foundations and practical applications of RAG, Langchain, LangGraph, and LlamaIndex
- Compare and contrast traditional RAG, RAG Fusion, and RAPTOR implementations for optimal information retrieval and processing
- Explore real-world case studies and hands-on demonstrations of AI-enhanced security and networking operations
- Discover how to implement RAG for dynamic information retrieval, re-ranking, and advanced automation in cybersecurity and networking scenarios
Learn how to use Large Language Models (LLMs) for both offensive and defensive cybersecurity operations, as well as networking implementations. It covers fundamental RAG concepts and progresses to sophisticated agent-based implementations using frameworks such as LangChain, AutoGen, and LangGraph. The hands-on labs provide practical skills for building secure AI systems, with real-world examples such as incident response, OSINT, and ethical hacking scenarios.
Skill Level
Intermediate
Course Requirement
Any Linux system with Python 3.x installed.
AI Agents and Agentic RAG for Cybersecurity
