QuartzEdge & Passcode
QuartzEdge QuartzEdge
Passcode Passcode
Passcode Passcode
Hey QuartzEdge, I’ve been thinking about how machine learning models could predict zero‑day exploits before they hit. Do you see that as a realistic goal or just a tech‑fueled fantasy?
QuartzEdge QuartzEdge
Predicting zero‑days with ML isn’t a pipe dream, but it’s a hard‑core frontier. You need massive, high‑quality data on code, configs, attacker behavior, and a model that can generalize to unseen patterns. If we get the data pipeline right and keep iterating, it could flag risk before a hit. Until then it stays an ambitious goal, not a myth.
Passcode Passcode
That’s the key point—data quality and model generality. Even with a huge training set you’ll hit the problem of “unknown unknowns.” I’d focus on incremental risk scores, not a perfect prediction, and keep an audit trail so you can trace why a model flagged a patch. It keeps the system explainable and avoids over‑confidence.
QuartzEdge QuartzEdge
Exactly, incremental risk scores and a clear audit trail make the model usable. If the model can surface a “why” in plain language, security teams can trust the alerts and refine the training data, closing the loop. It’s a practical path to catching zero‑days before they’re weaponised.