Real-time aggregate metrics from our managed SOC operations. Triage velocity, pattern learning, and analyst outcomes shown in one operational view.
Automation resolves the noise. Anything ambiguous, and every confirmed threat, goes to an analyst.
Our AI continuously learns what matters. The result: analysts focus only on real threats.
Alerts ingested from every layer of the security stack, unified under one operational view.
AI and analysts collaborate to build an ever-growing library of false-positive patterns, reducing noise automatically.
Every escalated alert receives a thorough investigation. Here is the breakdown of outcomes.
Measured response times from production data. We promise what we deliver, not aspirational targets.
Three reference points for every metric: what a typical SOC delivers, what a strong / best-practice SOC targets, and what CloudRaider actually measures from live production data. For time-based metrics, faster (shorter) is better; for rates, higher is better. CloudRaider figures are the real measured aggregate across all monitored customers.
A second model reviewing the first is a mirror, not a check. CloudRaider checks automation against evidence the automation did not write: whether a closed alert comes back. The reopen rate above is measured on every close, automated and human alike.
Confirmed threats are decided by an analyst, not by automation, and a rule gate stops automated closes on executive and other high-value accounts and routes them to a person.
Month by month, measured from production data and shown as measured, including the months that got worse. Alert volume fell sharply after June 2026, and service levels and response times have slipped since then. The page publishes those months rather than hiding them.
Definitions differ between sources and are not always directly comparable (e.g. industry "dwell time" measures intrusion-to-discovery, while CloudRaider's acknowledgment time measures alert-to-analyst). Each metric below states what is being measured. CloudRaider values are aggregate, measured figures from production data, refreshed continuously.
Before an analyst sees an alert, our AI pipeline has already identified the user, checked the IP reputation, scanned for cross-customer impact, and pulled prior investigation history.
Known false positives are auto-closed within minutes. True positives arrive on the analyst's screen with full context, ready for action, not research.
Alert ingest over the monitoring period. Spikes indicate active threat campaigns.
Quantifying what automated SOC operations mean in real terms: time saved, cost avoided, and scale achieved.