nicholasmagner

nicholasmagner / autonomous-soc-analyst

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Autonomous SOC Analyst (Agentic AI Security Automation)

10
1
100% credibility
Found Feb 20, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project demonstrates an AI assistant that queries security logs from Microsoft Defender, analyzes them for threats, and generates structured reports mapped to the MITRE ATT&CK framework.

How It Works

1
🔍 Discover the Tool

You find this helpful project on GitHub that uses AI to spot security dangers in your company's computer logs.

2
🔗 Link Your Security Logs

You connect your security activity records and an AI helper so the tool can check for threats.

3
🚀 Start the Assistant

You launch the simple program and it wakes up ready to help.

4
💬 Ask About a Suspicious Computer

You type a plain question like 'Check recent activity on this machine for anything fishy' and hit enter.

5
📊 It Gathers Recent Activity

The tool quietly pulls the latest logs from that computer to review.

6
🧠 AI Hunts for Dangers

The smart AI scans the logs, matches patterns to known attack tricks, and flags potential issues.

📋 Get Your Threat Report

You see a clear list of possible threats with explanations, risk levels, and next steps to stay safe.

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AI-Generated Review

What is autonomous-soc-analyst?

This Python project delivers an agentic SOC analyst that automates threat hunting across Microsoft Defender for Endpoint logs and Azure tables. Feed it a natural language prompt like "check suspicious processes on windows-target-1," and it queries Log Analytics via KQL, analyzes results with OpenAI models, maps findings to MITRE ATT&CK tactics, and outputs structured JSON reports with confidence levels, IOCs, tags, and action recommendations. It speeds up triage and reporting for SOC workflows while emphasizing human oversight and evidence-backed outputs.

Why is it gaining traction?

The killer hook is the end-to-end MITRE ATT&CK walkthrough in the README, complete with screenshots of real hunts across reconnaissance to impact—proving agentic AI can structure investigations without hype. Unlike generic log parsers, it uses tool-calling to auto-pick tables and fields, delivering colored terminal displays and JSONL exports ready for SIEM ingestion. Developers dig the transparent, reproducible agentic flow on autonomous systems GitHub.

Who should use this?

SOC analysts triaging MDE alerts, threat hunters querying DeviceProcessEvents or SigninLogs for lateral movement, and security engineers prototyping autonomous SOC platforms. Ideal for teams blending AI with Azure Sentinel or Defender without building from scratch.

Verdict

With 10 stars and 1.0% credibility score, it's early-stage and lacks tests or broad production hardening, but the detailed docs and working demos make it a solid prototype for agentic security automation—fork it to experiment, don't deploy to prod yet. (198 words)

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