Ghost in the Logs: A 100% Offline AI Security Log Detective
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine What I Built I built Ghost in the Logs , a privacy-first local AI log analyzer. It runs 100% offline via Ollama to prevent data leaks, featuring two modes: a Spooky Ghost Story mode and a professional SOC Cybersecurity Audit mode. Demo Samyuktha-CS / ghost-in-the-logs 👻 Ghost in the Logs:…
The Hacktoberfest Open-Source AI Challenge Week 4 introduced "Ghost in the Logs," an innovative privacy-first local AI log analyzer. This project is designed to run 100% offline and prevent potential data leaks by utilizing open-weight language models. The system offers two distinct modes: "Ghost in the Machine" mode, which personifies security anomalies and server bugs as a spooky ghost story, followed by an explanation of the fix; and "SOC Cybersecurity Audit" mode, delivering a structured threat severity rating, plain-English anomaly breakdown, and actionable remediation steps.
The architecture of Ghost in the Logs consists of system or authentication logs streaming into a Streamlit web UI (built with Python), interfacing with a local Ollama API on a localhost port, and processing data through a Llama 3.2 (1B) open-weight model. The development process involved installing Ollama, pulling the lightweight open-weight model, and accessing the full Python source code (main.py) on GitHub.
This initiative emphasizes the importance of open innovation in the AI sector, particularly in enhancing security and data privacy by avoiding the risks associated with cloud data exfiltration. The project's inspiration stemmed from making AI practical and safe for students and security engineers, effectively bridging the gap between a fun, spooky Halloween theme and actual cybersecurity challenges.
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