AI Agents and Agentic RAG for Cybersecurity (Video)

Description

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.

Related Learning:

• Sign up for more live trainings by Omar Santos: https://learning.oreilly.com/search/?q=omar%20santos&type=live-course&rows=100&language=en&publishers=Pearson

Skill Level

Intermediate

Course Requirement

Any Linux system with Python 3.x installed.

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Sample Content

Table of Contents

Lesson 1: Introduction to RAG in Cybersecurity

1.1 Introduction to Retrieval Augmented Generation (RAG)

1.2 Exploring the GitHub Repositories and Additional Resources

1.3 Embeddings and Embedding Models

1.4 Indexing Techniques

1.5 Vector Databases

1.6 Chunking Strategies

1.7 RAG vs. Fine-tuning

1.8 RAG, RAG Fusion, and RAPTOR

1.9 Running Open Weight Models with Ollama

1.10 Exploring Open WebUI and Other Ollama Plugins

1.11 Introduction to AI Agents and Agentic Implementations

1.12 Introduction to Agentic RAG

1.13 Introducing the Model Context Protocol (MCP)

1.14 Introducing A2A and AGNTCY

Lesson 2: Introducing LangChain, LangGraph, and LLamaIndex

2.1 Introducing LangChain

2.2 LangChain vs. LlamaIndex

2.3 Prompt templates and system prompts

2.4 Introducing LangSmith

Lesson 3: Prompt Engineering, Prompt Chains, and RAG Examples

3.1 Mastering Prompt Engineering

3.2 Exploring Basic Prompt Chain Examples

3.3 Creating Prompt Branching Chains

3.4 Exploring Parallel PromptChains

3.5 Creating a Basic RAG Application

3.6 Creating a Complete RAG Application

Lesson 4: AI Agents and Agentic Frameworks 

4.1 Introduction to AI Agent Frameworks

4.2 Surveying CrewAI

4.3 Introducing LangGraph

4.4 Exploring Examples of LangGraph in Action

4.5 Exploring an Example of Agents with MCP Servers

4.6 Securing Agentic Implementations

Updates

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