Hi HN, I’m Paul. I’m building Syd, an offline-first forensic workstation that orchestrates tools like YARA and Nmap through a GUI, using a local LLM to analyze the results without leaking data. It runs completely offline on localhost—no data is ever sent to the cloud, making it safe for sensitive investigations.

Here's a demo: https://www.youtube.com/watch?v=8dQV3JbLrRE.

I built this because while tools like YARA are powerful, managing rule sets and decoding hex strings is slow. AI is great at explaining malware signatures, but I couldn't use ChatGPT for my work because pasting potential malware or sensitive logs into a web form is a massive security risk. I needed the intelligence of an LLM but with the privacy of an air-gapped machine.

Under the hood, it’s built on Python 3. I use subprocess to manage the heavy lifting of the scanning engines so the UI (built with CustomTkinter) doesn't freeze. The "secret sauce" isn't the AI itself, but the parser I wrote that converts the unstructured text output from YARA into a structured JSON format that the local LLM can actually understand and reason about.

I’ve been using it to triage files for my own learning. In one case, Syd flagged a file matching a "SilentBanker" rule and the AI pointed out specific API calls for keylogging, saving me about 20 minutes of manual hex-editing. In the demo video linked, you can see this workflow: scanning a directory, hitting on a custom YARA rule, and having the local AI immediately analyze the strings.

Through this process, I learned that "AI wrappers" are easy, but AI orchestration is hard—getting the tools to output clean data for the LLM is the real challenge. I'd love to hear if there are other static analysis tools (like PEStudio or Capa) you consider essential for a workstation like this, or how you currently handle the privacy risk of using AI for log analysis.