Global Retailer Achieves 90% Faster Threat Detection with AI-Powered SOC

A global retailer with 2,500 stores and a major e-commerce presence was struggling with security alert fatigue. Their legacy SIEM generated over 10,000 alerts daily, but limited analyst capacity meant most received cursory review at best.

During the previous holiday season, a sophisticated attack on their point-of-sale systems went undetected for 72 hours despite generating alerts. The signals were present but buried in noise. The resulting breach exposed customer payment data and cost millions in remediation and regulatory penalties.

ELMET conducted a comprehensive assessment of their detection capabilities, identifying critical gaps in behavioral analytics, automated triage, and threat correlation. The legacy rule-based detection couldn't adapt to evolving attack techniques.

The new AI-powered detection platform established behavioral baselines for users, devices, and applications. Machine learning models identified anomalies that rule-based detection missed, while automated triage prioritized alerts based on actual risk rather than simple severity ratings.

Integration with threat intelligence feeds enriched detections with context about known attacker infrastructure and techniques. When similar patterns appeared in the retailer's environment, analysts received high-confidence alerts with full attack context.

Within six months, mean time to detect dropped 90%. Alert volume decreased 85% through intelligent consolidation and noise reduction. Most importantly, during the next holiday season, the new platform detected and blocked 47 breach attempts—including three sophisticated campaigns targeting their payment infrastructure.