3sk1nt4n

3sk1nt4n / cybersentinel-ai

Public

Agentic-AI CyberSecurity Arsenal || 33 real tools, runs 100% locally and 100% Free

61
15
100% credibility
Found Feb 17, 2026 at 46 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

CyberSentinel AI is a user-friendly security dashboard that lets you chat with AI to run real scans, check threat intelligence, and analyze logs from multiple monitoring systems.

How It Works

1
🔍 Discover CyberSentinel

You hear about this helpful AI sidekick that checks websites for dangers and explains security simply.

2
🚀 Start it up easily

You grab it and launch everything with a single button, watching services come alive.

3
🧠 Pick your thinking partner

Choose a local brain for privacy or connect a powerful cloud thinker to make it super smart.

4
Chat or run a check
🔍
Quick scan

Point it at a website and see live results from safe checks.

🛡️
Deep chat

Ask security questions and get expert advice with real data.

5
📊 Watch results appear

See colorful reports, graphs, and alerts showing what's safe or risky.

✅ Own your security insights

You now have clear reports, threat updates, and peace of mind about your online safety.

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Star Growth

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AI-Generated Review

What is cybersentinel-ai?

CyberSentinel AI is an agentic AI cybersecurity platform built in Python with a Next.js dashboard, running 100% locally via Docker Compose. Tell it to scan a domain or check threat intel, and it auto-detects intent, executes 33 real tools like Nmap, Nikto, Nuclei, SQLMap, Shodan, and VirusTotal in a Kali sandbox, then analyzes results with local LLMs (Ollama) or cloud providers. It solves the hassle of spinning up separate pentest tools, SIEM queries (ELK, Splunk, Wazuh), and intel lookups by unifying them in a chat interface with graph-based risk mapping.

Why is it gaining traction?

Unlike basic chatbots or paid SaaS scanners, this agentic AI cybersecurity GitHub project chains tools autonomously—say "full scan on example.com" for Nmap + DNS + SSL + headers—delivering real outputs without hallucinations. Local-first setup with Neo4j graphs, RAG knowledge base, and SIEM integrations appeals to devs avoiding cloud costs, while multi-provider AI (Ollama free, Claude/OpenAI optional) and PDF exports make agentic AI cybersecurity use cases instantly practical.

Who should use this?

Pentesters and bug bounty hunters needing a local arsenal for quick scans without AWS bills; SecOps teams prototyping SIEM queries or threat hunting playbooks; cybersecurity analysts mapping attack surfaces via graphs or pulling live intel from AbuseIPDB/OTX.

Verdict

Grab it for local agentic AI cybersecurity experiments—42 stars and 1.0% credibility reflect early maturity with solid Docker docs, but sparse tests mean tweak for production. Strong starter for Python/Docker devs eyeing agentic AI GitHub projects. (187 words)

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