The term "event correlation" refers to the automated linking and analysis of events (e.g. logs, alerts, status messages) from different systems in order to identify relationships, patterns or causal chains. The objective of event correlation is to derive meaningful information from a large number of individual messages – for example to detect incidents faster, reduce alert noise or identify security-related events.
Event correlation is mainly used in areas such as IT monitoring, network and system management, Security Information and Event Management (SIEM), AIOps and IT service management to operate and control business-critical applications and services more efficiently and transparently.
Collection and normalization of event data: Aggregation of logs, metrics, alerts and status messages from different sources (servers, applications, networks, security systems) and normalization of data formats.
Rule-based correlation: Definition of correlation rules (if-then logic, thresholds, patterns, sequences) to automatically group and assess related events.
Time-window and sequence analysis: Evaluation of which events occur within a defined time window or in a specific order to detect typical failure or attack patterns.
Topology- and dependency-based correlation: Use of service maps, infrastructure topologies or CMDB data to consider technical and business dependencies and distinguish true root causes from downstream effects.
Alert reduction (aggregation, deduplication, suppression): Consolidation of similar or recurring messages into a single incident, removal of duplicates and suppression of known follow-up or cascading alerts.
Prioritization and risk scoring: Scoring of correlated event groups based on criticality, affected services, SLAs, compliance requirements or security risk.
Automatic event enrichment: Adding context information such as asset data, location, user accounts, known error patterns, configuration data or existing tickets to events.
Integration into ITSM and security processes: Handover of correlated events to ticketing systems, incident and problem management as well as SIEM/SOC workflows, including automatic ticket creation.
Visualization & dashboards: Display of correlated event chains, service and dependency relationships, heatmaps and root-cause analyses in central dashboards.
Machine-learning-based correlation: Use of ML/AI methods to detect recurring patterns, anomalies or complex relationships that are difficult to capture with purely static rules.
Several failed logon attempts against a directory service, followed by a successful logon from an unusual country, are correlated into a potential brute-force or account takeover incident.
Simultaneous "interface down" messages on multiple switch ports are consolidated into a single line or provider outage based on the network topology instead of being shown as many individual alerts.
Increasing CPU and I/O utilization on a database, higher response times of a business application and a spike in service desk calls are correlated into a performance incident with the identified root cause "database".
An alert storm from server, storage and application monitoring after a power failure is grouped into a single major incident that automatically creates a ticket in the ITSM system and notifies the on-call team.
In a SIEM system, firewall logs, VPN connections and endpoint alerts are correlated to detect a targeted attack with lateral movement in the network at an early stage and escalate it with high priority to the Security Operations Center (SOC).