u/Cubeless-Developers

Had a really annoying afternoon last week. Sentry started lighting up after a deploy and it traced back to a changed value in one of our config files. Easy enough, I just needed to know who changed it and when.

GitHub's audit log showed the push, but it came through a token that a few different things use. No way to tell if it was a person, a pipeline, or some script somebody wrote months ago and forgot about. I ended up lining up commit times against CI runs and deploy timestamps for like an hour, and I'm still not totally sure I got it right.

Honestly kind of irritating. If the logs can't tell me who or what was behind a change, I'm not sure what we're keeping them for.

How are you all handling this? Is there a setup that ties a change back to a specific person or job, or is everyone just matching timestamps and hoping?

We've been letting AI tools touch auth and access control code for a while now, and it's turned into a real debate on our team about where the line should be. The output's usually fine. But nobody's comfortable merging anything auth-related without extra eyes on it first.

Right now that means automated scanning on every commit, plus a mandatory human review for anything touching auth. It works, mostly. Just adds friction.

What I can't figure out is whether that's real control or just a checklist that makes us feel better about it. Has anyone built something more structured for AI-generated security code specifically?

Ran a quick audit last week to see what AI tools people were actually using versus what IT knew about. The gap was bigger than I expected, not gonna lie.

People are stitching things together in ways that never show up in any log we have. Browser extensions, personal API keys, random Chrome plugins that pipe data somewhere. Can't really blame them either. The tools actually help them get work done faster.

Problem is we have zero visibility into what data is leaving and where it's landing. Not even a rough guess.

Still trying to figure out how you get ahead of this without turning into the department that says no to everything. Anyone actually cracked this, or is everyone just quietly panicking like us?

We started giving our internal AI agents scoped permissions and periodic audits, treating them like service accounts. Still feels like we're guessing.

Once an agent connects it can read data, trigger workflows, and call APIs, and nothing's asking for per-action approval. We've been trying to catch overprivileged connections before they ship, but honestly our CI/CD pipeline wasn't built with agent identity in mind.

Anyone actually solved this, or are we all just hoping for the best?

We've been tightening up our MFA requirements lately, and it's starting some interesting debates internally. Basically: does every internal tool need MFA, or do you draw the line somewhere based on what it touches and who's using it?

At a sub-500 person org where IT bandwidth is thin, the friction argument comes up a lot, so how are other teams drawing that line?

We've started giving AI agents access to internal tools and realized they're inheriting full user-level permissions with no guardrails. Nobody questions what they can read, write, or delete.

Is anyone actually scoping AI agent access deliberately, or is full inherited access just becoming the default? Curious how teams are thinking about this.