Aidan McLaughlin
I'm a detection engineer based in Glasgow and a former Principal Security Engineer at Oracle. I've spent the last seven years working across banking, enterprise, and startup security teams, and currently lead detection at Alpha Level, an ML threat detection startup, alongside independent consulting work. I'm mostly drawn to the parts of security operations that don't have tidy answers yet.
Session
AI models like Mythos are finding exploitable vulnerabilities faster than the industry can disclose, patch, or write signatures for them. The inevitable consequence: a flood of 0-days in the wild. Every signature-based detection you own is, by definition, blind to them. This talk makes the case that statistical anomaly detection is no longer an optional "ML in security" side quest. It's the only class of detection that can catch the exploitation of things nobody knows exist yet. Drawing on production experience building ML detection, we'll cover what works, what doesn't, and why your UEBA tab isn't going to save you.