AI Threat Detection Engine

The security team was buried. 50,000 alerts a day, and 9 out of 10 turned out to be nothing. By the time they finished investigating false alarms, actual threats had already done damage. I built a detection system that actually thinks. Using Vertex AI, we trained models on their historical incident data—what real attacks looked like versus the noise. The system learned patterns that humans miss when they're exhausted from alert fatigue. We integrated it with their existing SIEM so they didn't have to rip and replace everything. The AI watched traffic patterns, user behavior, and system anomalies across their entire cloud and on-premise infrastructure. When it spotted something genuinely suspicious, it would automatically kick off response playbooks instead of just sending another email nobody reads. The results were honestly better than expected. False positives dropped from 92% to 27% in the first 2 months. More importantly, detection time went from over four hours to under fifteen minutes. The SOC team could finally focus on actual security work instead of babysitting alerts. Six months in, they haven't had a single critical incident slip through.