AI-Driven Threat Hunting: LLMs, Agents & Security Workflows | Intro Video

Описание: Hello Hunters,
It's been a while. Life happened, couldn't keep up the pace — but I'm back, and this time I'm trying something different. No more multi-part series that leave you waiting forever for the next drop. Every video from here will be self-contained and to the point.

Now, we've covered a lot of ground on threat hunting — methodology, hypothesis building, hunt planning. But today I'm pivoting hard. The entire security landscape is shifting, and if you're not paying attention to AI right now, you're genuinely falling behind. Not in a hype-y buzzword way — I mean practically, in your day-to-day work.

Think about it like this: cloud changed how we deployed. Compilers changed how we wrote code. AI is doing the same thing to how we do analysis, investigation, and detection. And whether it's a bubble or not — which I don't think it is — the cost of trying it out is basically zero. The upside isn't.

In this episode I'm not diving into workflows or automation yet. Before we do all that, I want to make sure we're all on the same page on the fundamentals — what LLMs actually are, what an agent really means (not the buzzword version), and what tools you can actually use today to start integrating this into your security work. Because the gap between people who understand these basics and those who don't is going to get very wide, very fast.

One thing I'll say upfront though — AI doesn't replace fundamentals. It amplifies them. If you don't understand how to build a hypothesis or what threat intel actually looks like, the AI isn't going to save you. Think of it like having a team of interns. You can only direct them well if you know what good looks like.

🎯 What's covered in this one:
Why AI adoption matters right now — practically, not philosophically
How it touches every security vertical: IR, DFIR, CTI, SOAR, Malware Analysis
What LLMs are, how they actually work (keeping it simple)
The model landscape in 2026 — GPT, Claude, Gemini, DeepSeek, and where each shines
What an Agent actually is vs. just an agentic workflow (this distinction matters a lot)
Tools to consume AI: IDEs, CLI tools, and chat apps — and why CLI is where the real magic happens
Subscription options including India-specific free tiers (Jio Gemini Pro, ChatGPT Go, Airtel Perplexity)

📋 Key Concepts:
✅ LLMs — next-token prediction, reasoning models, context windows
✅ Agent = LLM + Tools + Planning + Execution (and what that actually means)
✅ Agentic workflows vs. true autonomous agents — the LangGraph mental model
✅ CLI tools: Claude Code, Open Code (open source), Gemini CLI, Codex
✅ IDE tools: Cursor, Windsurf, VS Code + GitHub Copilot, Agentic AI by Google
✅ Which models to use for what kind of work
✅ How to get started without spending much — especially if you're in India

🔧 Tools mentioned:
Claude Code / Claude.ai
OpenAI Codex CLI
Gemini CLI + Agentic AI
Open Code (open source, honestly underrated)
Cursor, Windsurf, GitHub Copilot
ChatGPT, DeepSeek, Gemini (chat interfaces)


🔗 Resources:
LangGraph (Agents vs. Workflows): https://www.langchain.com/langgraph
Open Code: https://github.com/opencode-ai/opencode

📈 Level: Beginner–from zero
🎯 For: Threat Hunters, SOC Analysts, Detection Engineers, DFIR folks curious about how to leverage AI

Subscribe and hit the bell 🔔 if you want to follow along as this series builds out.
Drop your questions below — I read every comment.

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