LLMs can generate powerful answers — but what if we want an AI security system to answer using our own security knowledge?

In Video 14, we build a practical AI Security RAG using Python, Gemini and ChromaDB, with sample CVE and MITRE ATT&CK security knowledge.

This is Video 14 of the AI-Driven Cyber Security Complete Course 2026 — Phase 2: AI & LLM Fundamentals for Security Engineers.

WHAT YOU'LL LEARN
* What is RAG (Retrieval-Augmented Generation)?
* Why RAG is important for Cyber Security
* What CVEs are and how vulnerability information can be represented
* What MITRE ATT&CK is and how techniques like T1059 are used
* How security knowledge can be stored in ChromaDB
* How semantic/vector search retrieves relevant security information
* How retrieved context is passed to Gemini
* How to make AI answers more grounded in your security data
* How the complete RAG flow works from question → retrieval → AI answer

WHAT WE BUILD
A complete beginner-friendly Security RAG pipeline:
User Question
↓
ChromaDB Vector Search
↓
Relevant Security Context
↓
Gemini
↓
Grounded AI Security Answer

We build the project step by step in Python, explain the code line by line, and run the complete RAG system live.

BY THE END OF THIS VIDEO
You'll understand how RAG works and have built a working foundation for AI-powered Cyber Security applications such as AI SOC Analysts, Security Knowledge Assistants and Threat Intelligence tools.

Note: The CVE entry used in the practical is sample/example security data for demonstration.

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CONNECT & LEARN WITH US
Website:
https://www.thetechzeen.com/

Full AI-Driven Cyber Security Playlist:
https://www.youtube.com/playlist?list=PLMnWzLCjYQQk

Source Code:
https://github.com/farzeen-ali/chromadb-with-python

Lecture Notes:
https://github.com/farzeen-ali/AI-Driven-Cyber-Security

Cyber Security WhatsApp Community:
https://whatsapp.com/channel/0029VbCpEz6JkK75tDMYj43M

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