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Concept for systematically detecting operational outages, performance deviations, and security incidents based on observability signals and defined alerting criteria.
Incident detection describes practices and principles for the early identification of operational outages, security incidents and performance deviations based on observability signals. It focuses on structured metrics, logs and traces and on defined alerting criteria to reduce response time and limit impact. Approaches range from rule-based alerts to statistical anomaly detection.
Average time from occurrence to detection of an incident; measures detection capability.
Share of alerts that are not real incidents; affects operational burden.
Percentage of critical services with sufficient telemetry and alerting.
Prometheus metrics combined with Alertmanager rules provide fast, deterministic detection of CPU and error thresholds.
Statistical models or time-series algorithms detect deviations from baselines and reduce false positives under variable loads.
SIEM platforms correlate logs, network events and IOC data to improve detection and prioritization of security incidents.
Instrument critical paths with metrics, logs and traces
Define baselines, thresholds and escalation rules
Introduce alert channels and on-call processes
Iterative tuning and validation via game days and postmortems