A continuous security monitoring platform that centralises infrastructure logs, applies ML-based threat detection and automates incident response playbooks.
Representative engagement — anonymised and illustrative. It describes the kind of problem we solve and the platform we build, not client-confidential performance data.
A mid-size technology company with 200+ servers and cloud instances had no centralized security monitoring. Their SIEM was misconfigured and generated so many false positives that the security team had effectively stopped reviewing alerts. Two incidents in the past year went undetected for over two weeks.
We built a custom security monitoring platform that aggregates logs from all infrastructure, applies ML-based threat detection to reduce false positives, provides real-time dashboards with severity scoring, and automates incident response playbooks. The system integrates with their existing tools — Kubernetes, cloud providers, and endpoint agents.
Centralised log aggregation brings all infrastructure into one security view
ML-based detection cuts the false-positive noise that had trained analysts to ignore alerts
Severity-scored dashboards make triage decisions fast and defensible
Automated response playbooks handle common threat patterns consistently
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