Detecting ICT Incidents Early

Detecting ICT incidents early means noticing that something is wrong with a service or its systems while there is still time to contain the effect. Detection relies on monitoring, logging, and alerting that are tuned to the service, its normal behaviour, and its risks. The aim is not to collect every possible signal. It is to surface the signals that indicate a service is failing, under attack, or behaving in a way that precedes failure.

Early detection matters because the cost of delay grows with the spread of an incident. A compromised account can be contained before it reaches other systems. Corrupted data can be isolated before it propagates. A failing component can be rerouted before customers experience a widespread outage. Detection is the bridge between protection, which tries to prevent the incident, and response, which contains it.

Detection watches systems, networks, data, and user activity for signs of failure or compromise. It watches for unusual access, for changes to critical configuration, for errors in processing, for performance degradation that signals a failing component, and for patterns that match known attack techniques. It also watches the service itself, because a service can degrade in ways that no single system alert captures, such as a slowdown that affects customers across several channels.

The service lens prevents detection from becoming a purely technical exercise. An alert about a single server matters differently when that server sits on the path of a critical payment service than when it supports an internal reporting tool. Detection design therefore starts from the service map, prioritising the signals that would warn of harm to important services, and accepting that lower impact signals can be handled with less urgency.

Logging records what happened, in enough detail and with enough integrity for an incident to be reconstructed afterwards. Monitoring interprets those records, and live system behaviour, in near real time, and raises alerts when a pattern departs from normal. Without reliable logs, monitoring can alert but cannot explain. Without monitoring, logs become an archive that is read only after harm has spread. The two are complementary, and weakness in either weakens detection as a whole.

Log integrity deserves particular attention. Logs that an attacker can alter or delete lose their value as evidence and as a detection source. Protecting logs, retaining them for a period that allows incidents to be understood, and being able to correlate logs across systems and suppliers are practical conditions for detection to work. A detection story that assumes logs are complete, without showing how their integrity is protected, rests on an unexamined assumption.

Alerts fail most often through volume and vagueness. A team flooded with low value alerts learns to ignore them, and a genuine signal can be missed among the noise. An alert that says only that something is unusual, without saying which service is affected, what changed, and what the responder should check first, delays response even when it is noticed. Alert tuning is therefore detection work, not housekeeping after detection is built.

Alerts succeed when they are tied to risk and to response. An alert linked to a service, a plausible cause, and a first action gives a responder a starting point. A pattern of alerts that is reviewed over time, with noisy rules refined and silent gaps investigated, gradually improves. Detection quality is visible in the record of alerts that were acted on, alerts that were false, and risks that produced no alert at all. That record is how detection learns.

Suppliers affect detection because a service chain often extends beyond the entity itself. An incident in a supplier system may first appear as a service degradation, a data error, or a missing feed, rather than as a clear supplier alert. Detection therefore needs arrangements for supplier notification, for correlating supplier information with internal monitoring, and for recognising supplier incidents from their service symptoms when direct notification is delayed.

The difficulty is that supplier visibility is partial. An entity cannot monitor a supplier system as directly as its own. Contracts, reporting arrangements, and shared incident handling processes become part of detection, not merely commercial terms. A detection design that assumes full visibility into every supplier system overstates what is possible, while a design that ignores supplier signals altogether leaves a large part of the service chain unwatched.

Detection feeds response through the quality of the handover. A detection that names the affected service, the systems involved, the time the anomaly began, and the evidence available lets response start with containment rather than with investigation from scratch. A detection that merely reports an anomaly leaves response to spend its most valuable period reconstructing what detection already knew but did not pass on.