A Top‑Tier Security Firm needed a cleaner, faster way to identify compromised systems across a large, distributed environment. Traditional monitoring was reactive, slow, and produced excessive false positives, delaying response.

Design BEACON, a machine‑learning platform that could proactively detect compromise in near real‑time by analyzing changes in network behavior, at global scale and with minimal analyst overhead.

• Started from the assumption that compromised systems change their network footprint in measurable ways.
• Analyzed the first and second derivatives of network traffic to expose rapid shifts and emerging patterns.
• Detected predictable malicious behaviors such as C2 (command‑and‑control) communications, unauthorized scans, and probing activity.
• Built lightweight telemetry agents feeding a real‑time anomaly detection engine.
• Tuned ML models for high precision to minimize false positives.
• Integrated streaming analytics with SOC workflows and orchestration/ticketing for automated response.

• 97% faster detection of compromised systems versus legacy tools.
• Identified insider threats and beaconing activity previously missed by traditional monitoring.
• Reduced median detection times to near real‑time.

• Reduced analyst workload by 85% through fewer false positives and streamlined triage.
• Lowered breach remediation costs by up to 17×.
• Significant improvements in overall SOC efficiency and incident impact reduction.

Proved the value of operationalizing ML for real‑time security detection at scale, enabling broader automation across business units and delivering a measurable uplift in cyber resilience.