What are the most common mistakes when interpreting logs?¶

The recurring mistake is to read a technical record as a complete human narrative. Logs describe what configured systems observed; identity, intent, completeness and real-world outcome need separate analysis.

Labels and identifiers are often overstated¶

An account is treated as a person, an IP address as a location, a device name as a physical endpoint, or authentication success as legitimate use. Alerts are mistaken for source evidence and severity for maliciousness. Automated scripts, services, APIs and synchronisation are overlooked.

Timing errors include mixing event and ingestion time, ignoring zones and clock quality, or inventing order between equal-precision values.

One displayed dataset is rarely the whole picture¶

Client, server and database records can represent request, receipt and completion. Dashboards and SIEMs may filter, normalise or enrich source events, while retention and collection gaps limit coverage. Field definitions, source records and correlated stages are therefore essential where the conclusion matters.

Collection should also answer a defined question. More sources do not automatically add value once material alternatives and evidential limits are understood.