Alert correlation is the grouping of related alerts, often from different monitoring tools, into a single incident, so one underlying problem produces one actionable signal instead of dozens.
In most enterprises alerts come from many tools at once, infrastructure monitoring, APM, logs, security, and a single incident lights up all of them. Without correlation, on-call drowns in fragments of one problem. Correlation stitches those fragments back into the incident they belong to, using time, topology and pattern, across tool boundaries.
Each tool has its own alert format, its own idea of a host or service, and no knowledge of the others. Correlating across them needs a shared model, a common notion of the entities involved, so an APM alert about a service can be linked to an infrastructure alert about the node it runs on. Without that shared model, correlation is guesswork.
Related alerts tend to share a signature. Temporal correlation groups alerts that fire together in time. Topological correlation groups alerts on connected entities (a service and its database). Pattern or ML correlation learns which alerts historically co-occur. The strongest approaches combine all three.
Ops Singularity ingests signals from the tools you already run through Integration Connectors, normalises them onto a shared entity model, and Sentinel AI correlates them by time, topology and learned pattern into a single explained incident, then resolves it through governed Action Tickets.
Because a single problem triggers alerts across many tools that do not know about each other. Correlating them onto a shared model collapses the storm into one incident.
By grouping related alerts using temporal proximity, topological connection between entities, and learned co-occurrence patterns, across tool boundaries.
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