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Detecting Threats in Real Time: How We Built a Threat Analysis Engine That Processes 2M Events Per Second

Key Outcome
90 sec
mean time to detection — down from 6+ hours
Team
16 engineers
Timeline
16 weeks
Industry
Cybersecurity
01The Situation

A cybersecurity firm serving mid-market enterprises — companies too large to ignore security and too small to build a full SOC internally. They offered managed detection and response (MDR) but their analysis engine was falling behind. Their client base had grown 4x in two years. Their detection pipeline hadn't scaled with it.

02What Changed

Alert fatigue. The system was generating 50,000+ alerts per day across their client base. Analysts were triaging manually. Mean time to detection was measured in hours, not seconds. A client was compromised through a credential-stuffing attack that had generated alerts for 6 hours before an analyst got to it. The attack was successful because 6 hours was enough time to escalate from initial access to lateral movement to data exfiltration.

03Why The Algorithm

They needed an engineering team that could build a detection pipeline capable of processing millions of events per second with sub-second alert correlation — not a security consulting engagement, an engineering build.

04What We Built

Real-time threat analysis engine. Stream processing pipeline ingesting network traffic, endpoint telemetry, authentication logs, and cloud audit trails. Correlation engine identifying multi-stage attack patterns across data sources in real time. Behavioral baseline modeling per client environment — detecting anomalies against what's normal for THAT network, not generic rules. Automated triage classifying alerts into critical, high, medium, and informational. ML models trained on confirmed incidents to continuously improve classification accuracy.

05 — The Result

Processing capacity: 2M+ events per second, up from 200K. Mean time to detection reduced from 6+ hours to under 90 seconds for critical threats. False positive rate reduced by 73% through behavioral baselining. The firm's client capacity tripled without adding analysts because the automation handled what humans couldn't scale.

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