Event Logging
Event logging means that a computer program continuously records what it does: who logged in, which file was opened, where an error occurred. These records are the most important basis for tracking down disruptions, detecting attacks, and later proving what actually happened.
Every larger computer program writes down in the background what it is currently doing. This produces lines such as: “14:02:11 — user Meier logged in” or “14:02:19 — payment failed, reason: timeout”. This very act of recording is called event logging. A single entry is called an event, and the collection of all entries is called a log, or in German a Protokoll. You can picture it like the logbook of a company car: who drove where and when, and what was noticeable along the way. The purpose is the same — you want to be able to reconstruct later what happened, even if no one was watching at the time.
Without a log, there is no troubleshooting
Today, software runs on many machines at once, often in data centers belonging to someone else. No one can watch what happens at the exact moment something goes wrong there. If a bank customer reports that their transfer has vanished, the log is the only trace. Technicians search it for the relevant point in time and read backwards to see which step failed. Without logging, all that remains is guesswork.
The second reason is security. An attacker who steals a password leaves traces behind: unusual login times, access from foreign countries, hundreds of failed attempts within a minute. Such patterns only become noticeable if someone collects and evaluates the events. Many break-ins are only discovered weeks later — and specifically, in the logs.
Then there is a legal reason. Banks, hospitals, and government agencies must be able to prove who accessed which data. Such especially protected logs are called audit logs. They must not be altered afterwards, or else they would be worthless as evidence.
From the individual entry to the central collection point
A log entry usually consists of four parts: timestamp, source, severity level, and description. The severity level indicates how urgent the matter is. Common levels are “debug” for technical details, “info” for normal operation, “warning” for anomalies, and “error” for actual failures. Developers configure from which level onward events are recorded. Recording everything would be expensive, after all, since logs can fill up many gigabytes per day.
In the past, the lines simply ended up in a text file on the same machine. That is no longer sufficient today, because a service runs on dozens of machines simultaneously. That’s why all machines send their events to a central collection point. There, they are made searchable, similar to a search engine. A technician can then ask: “Show me all errors from the last hour concerning user Meier.”
Modern logs are often stored not as plain running text but in structured form, with clearly named fields. That may sound like a minor detail, but it is crucial: only this way can a program automatically evaluate the millions of lines. A common misconception is that logging is the same as monitoring system performance. Metrics like memory usage tell you that something is slow. The log tells you why.
Where you encounter logs in everyday life and in the news
You see the results of event logging more often than you might think. The email “New sign-in from an unknown device” arises from a login event. The list of active sessions in your Google or Instagram account is a tidied-up view of such entries. Even a package’s shipment tracking history is, at its core, an event log.
In business news, logs come up after cyberattacks. It is then often said that a company cannot “conclusively reconstruct” the extent of a data breach — usually because logs were missing or were deleted too early. Conversely, companies like Splunk, Datadog, or Elastic earn a great deal of money by collecting and analyzing logs for large corporations. The market for this is a billion-dollar business.
With AI systems, the topic is gaining additional importance. When a language model provides an answer, one wants to know afterward which query was made and which data the model drew upon for it. The European AI Act explicitly requires such records for high-risk applications. Logging has thus turned from a technical chore into a legal obligation.
