Matthew Evans

I audited an AI-written billing service this week. 22 findings, 10 critical.

None of them crashed. All returned 200. Four that stuck with me: an auth check that silently lets anyone in when one env var is missing, SQL injection in the one query that wasn't parameterized, a broken permission boundary so anyone can attach a charge to a workspace that isn't theirs, and a Stripe retry that double-grants seats because there's no idempotency check anywhere.

If you ve been following along and want to support us, we d really appreciate a GitHub star:

https://github.com/ota-run/ota

We just crossed 50 followers here on Product Hunt! Thank you so much for every follow, comment, question, and bit of feedback. For an early product, it really does mean a lot.

As a small gesture of appreciation, here s a fun little backstory: we chose an otter as our logo because otters sleep floating on water while holding hands so they don t drift away with the current.

On a Tuesday, the first enterprise question is usually not can you capture AI code? It s who can see the records, how long do they live, and what happens when a policy blocks a change?

That s the part LineageLens is built for. Base gives you local capture. Lite gives a shared team record. Plus and Max move the data into a backend where auth, permissions, retention, and policy live next to the provenance records instead of around them.

The useful thing here is not another dashboard. It s a self-hosted record of prompt, model, tool, file, and outcome that engineering, security, and platform teams can actually govern on their own infrastructure.

I keep seeing AI governance tools start with visibility, then discover that the real enterprise questions are identity, retention, and review. If the record cannot be scoped, retained, and exported on your side, it is not really governable.