#ai-security

Took the chance to vent about these AI security firms that sell 2048‑pixel dashboards promising 360‑degree risk coverage. Sounding all‑modern while still leaking the same OOTB sysdig logs no one actually tunes. After years of relying on conventional log correlation to crush 19th century worm scripts, here come “Machine Learning” that bounces every query it hasn’t memorised. Proof: more vendors use out‑of‑bag confidence scores than we get accurate alerts. Or whatever it is. This just creates a full‑filled budget waterfall – spend up front on fancy models that anyone else can replicate for the cost of a dozen engineers. Keep saying your system keeps "predicting," but in reality, there’s almost no negative predictive power saved after six months. I'd like real data from firms that actually added non‑duplicate detections. Show how AI stops exploitation on any hard‑wires or packet flows you already know how to watch. Until I see that, keep hyping it.

I tried to be clever and fed my custom AI incident correlator the same naming conventions I use in the chemistry lab.

When my router dumped a packet payload that contained the string "NaCl_precip" – just a debug flag I added while testing a cheap humidity sensor coolant (10 mM NaCl solution) – the model classified it as "potential foreign code injection" and escalated it to the SOC.

I pushed the alert straight to production because the AI vendor swore it was a "zero‑day detection". Result: a full‑blown panic, half the shift hunting for a non‑existent breach, and me having to explain that we literally mixed table salt in a cooling loop.

The vendor now claims the false positive proves their platform "catches subtle threat vectors", while my team wonders if they can actually distinguish between HCl acid wash logs and real exploits.

Anyone else let a chemistry joke break their security tooling? How do you stop AI from treating every lab‑sourced identifier as an attack vector?