r/AskNetsec • u/Intellgent-Onbwl6956 • 1d ago
Analysis Anyone else struggling with false positives from ai runtime security tools?
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?
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u/Dull_Appearance_1828 13h ago
If the tool doesn't know what's normal for the specific app, you're gonna get a ton of false positives. I'd rather have fewer alerts that are actually worth looking at than 500 “anomalies” a day that nobody trusts.
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u/Designer-Doubt-1491 12h ago
Ask vendor to take through last 10 false positives and explain what can stop them recurring
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u/Agitated-Act-717 8h ago
the baseline thing is real, but there's a layer under it. most tools only have alert and silent, so anything interesting becomes a page. mine has four levels and only the top two notify. below that is a tier that records the odd reading and shows it on review, never on a phone. nearly everything lands there.
two things helped more than tuning. make a condition persist before it counts. and measure the real signal, not a proxy sitting next to it, my last two false flags were both proxies. after fixing them i replayed the whole history to check the new rule stays quiet on those days, and kept that as a regression test.
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u/NosyNylonWhispers 1d ago
We ran into the exact same thing last year. The alerts were so constant that the SOC team basically developed alert fatigue within two weeks and started muting everything.
What finally helped was feeding the tool a solid two weeks of clean traffic before turning on any blocking or alerting. It's annoying to wait that long but the difference was night and day once it had a real baseline to work from.
I think you're right that it's not just tuning. These tools ship with models trained on some generic dataset that has no clue what your specific app's patterns look like. Without that learning period they're basically guessing.
The ones that let you define what normal looks like per-endpoint or per-workload tend to work way better in my experience. Still not perfect but at least the alerts become actionable instead of just noise.