AI doesn't search like a traditional database — it can search by meaning. Today, we're seeing that happen for real.

Welcome to Video 13 Practical of the AI-Driven Cyber Security Complete Course 2026 — Phase 2: AI & LLM Fundamentals for Security Engineers.

In this hands-on practical, we build a working semantic search system using Python and ChromaDB, a vector database designed for AI applications.

WHAT WE BUILD:
Create a Python project and virtual environment
Install ChromaDB
Create a persistent ChromaDB client
Create a security collection
Store security-related documents
Perform semantic similarity search
Query the database using natural language
Retrieve the most relevant security documents
Understand persistent vector storage

We also go through the Python code line by line and run the complete project live in Kali Linux + VS Code.

The interesting part?

Instead of searching for an exact keyword, we ask:

"How do hackers steal passwords?"

And ChromaDB retrieves security information related to phishing and brute-force attacks based on semantic similarity.

BY THE END OF THIS PRACTICAL:
You'll have built your first local vector database and semantic search system — an important building block for the RAG and AI security systems we'll build later in this course.

This practical continues the Embeddings & Vector Database concepts from the previous lesson.

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Website:
https://www.thetechzeen.com/

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

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

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

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

#ChromaDB #VectorDatabase #Python #SemanticSearch #Embeddings #AIDrivenCyberSecurity #RAG #CyberSecurity #TheTechzeen