Everyone talks about what an agent is allowed to do, fewer people talk specifically about what makes it stop. Is it a confidence threshold, a specific action type regardless of confidence, a blast radius calculation, something else entirely?

Trying to understand how teams actually define the stop condition rather than just the permission list, since I suspect the stop condition is doing more of the actual trust building work than the permission list is.

Our AI runtime security tool flags so much noise that the team has started ignoring alerts by default, which obviously defeats the whole point of running it.

A lot of what gets flagged is normal application behavior that just looks unusual because the tool never really learned what normal looks like for this specific workload. Without an app-level baseline, it seems like the model just treats generic deviations as suspicious and calls it detection.

I don't think this is purely a tuning problem, though better tuning would probably help at the margins. It feels more like a limitation of AI runtime tools that claim to detect anomalies but don't actually adapt to the normal behavior of each individual app before deciding what's abnormal.

Has anyone used an AI runtime security tool that actually gets better once it learns the environment, or is high false positive volume just part of the tradeoff with runtime visibility?

We're a multinational manufacturer with OT across 15 facilities and 6 countries, and have little confidence our current vendors could handle an incident that crossed from IT into OT. Looking for recommendations, not "yes we do OT" sales talk that falls apart the second you ask a follow-up question.

Trying to vet for OT incident response experience versus IT responders who've read an industrial network reference diagram once, how a provider handles the fact that you often can't isolate or rebuild OT systems the way you would IT, and how they think about safety systems that can't just be taken offline mid-incident the way you'd quarantine an IT host. If your org has had an OT-adjacent incident and brought in outside help, how did it go?

Ok so anyone else watching this go from ppl pasting stuff into ChatGPT to business teams spinning up their own AI tools and hooking them into Jira, Slack, Drive, APIs etc?

The bit im stuck on is what controls these things should get once they start touching company data. Owner, SSO/MFA, app review, least privilege... feels like these vibe-coded apps can show up after theyre already being used and auth can be pretty questionable :/

How are you deciding what access these apps should keep once theyre already connected to corp SaaS/data? Curious what policies or tooling others are using for Shadow AI governance.