Catch risky code changes before your customers do.
A managed daily repository scan for founders and small teams using AI-assisted development. We check the code, surface the real risks, and give your developer or AI agent a clear fix prompt.
This is not a once-a-year PDF. It is a practical watch layer for repositories that are moving fast, especially when AI agents are shipping code.
Know what changed before it becomes a client problem
Every day, the scanner checks the repository for the code patterns that most often turn into real security issues: leaked secrets, unsafe service-role usage, exposed webhook paths, weak tenant boundaries, risky file uploads, and public access mistakes.
Get the right alert without reading a giant report
Critical items trigger a direct alert. The daily summary keeps the owner informed without forcing them to dig through raw scan output unless something needs action.
Give your AI coding agent a useful fix prompt
Findings include the affected file, the reason it matters, the likely fix, and a copy-ready prompt for a coding agent. Less vague security advice. More clean handoff.
Protect the portal like client data matters
Each client gets a secured portal with current status, history, notifications, and remediation prompts. No public report links. No raw secret values stored in portal output.
What the daily scan includes
Daily repository scan against the configured GitHub repo
Secret and credential exposure checks
Auth, webhook, cron, RLS, service-role, and middleware risk patterns
AI-agent and tool-approval safety checks
Dependency manifest watch and npm audit summary when available
Customer portal with current findings, history, and copy-ready prompts
Critical alerting and daily email summary
Scanner tuning that recognizes real remediation evidence without hiding raw diagnostics
How it works
We connect read-only GitHub access to the repo.
The scanner runs daily and publishes the latest status to the client portal.
Critical issues trigger direct alerts. Normal changes roll into the daily summary.
The client gives the finding prompt to their AI agent or developer, then the next scan verifies the fix from code and tests.
The report is built for action, not theater.
Each finding keeps the important pieces together: affected file, evidence, impact, recommended fix, and a prompt that can go straight to a coding agent.