AI-Powered Threat Detection — Beyond Rules
Your SIEM has 4,200 correlation rules. Your SOC analysts triaged 11,000 alerts last month. They investigated 900. They escalated 47. Of those 47, exactly 12 were true positives. The math is brutal: a 0.1% true positive rate across all generated alerts, and an analyst team spending 96% of its time chasing phantoms.
Rule-based detection served us well for two decades. Signature matching catches known malware. Correlation rules flag known attack patterns. Threshold alerts fire when login failures exceed a count. But the threat landscape shifted beneath our feet. Adversaries adopted living-off-the-land techniques that look identical to legitimate administration. Polymorphic malware mutates faster than signatures propagate. Zero-day exploits arrive with no signatures at all. And the sheer volume of telemetry — billions of events per day in enterprise environments — overwhelms any static ruleset.
Machine learning does not replace rules. It fills the gaps that rules cannot cover: the unknown unknowns, the subtle behavioral shifts, the patterns hidden in dimensionality that no human analyst could manually correlate across a million daily events.
This post covers the practical reality of deploying ML-based detection in security operations — the paradigms, the architectures, the pitfalls, and a full synthetic case study where a fictional financial services firm caught an APT that 4,200 rules missed.