A bad sourcetype
echoes for years.
We connect data sources, normalise them and watch that the flow keeps running, so that what goes in is correct. A source that is onboarded sloppily only shows up years later: when a detection fails to fire, or an auditor finds a gap. Data quality is therefore part of the service, not a clean-up job afterwards.
Managed Data Services.
Data that is correct, and stays correct. From onboarding a source to normalisation, enrichment and governance. This is the layer that reports and detections get their reliability from.
- Onboarding of new sources, from first conversation to working feed
- Parsing and normalisation so fields mean the same thing everywhere
- Data quality monitoring: completeness, timeliness and duplicates
- Data modelling, so search stays fast as volume grows
- Retention and provenance recorded, per source and per purpose
- Governance: who may see which data, and what must never go in
A bad sourcetype echoes for years.
Onboard a source sloppily and you find out when a detection fails to fire or an auditor finds a gap. Fixing it then costs ten times what doing it properly would have.
Discuss your questionEverything above it rests on this.
Dashboards, detections and reports are only as reliable as the data underneath. That sounds obvious until you have a detection that stayed silent because one field was named differently.
A detection is as good as its input.
Missing or badly parsed fields do not produce an error. They produce silence, and that is the most dangerous kind of failure.
Evidence with provenance.
In an audit it matters not only what is there, but where it came from and how long it was kept. You record that in advance or not at all.
Volume is not quality.
Ingesting everything makes search slower and more expensive. The question is not how much you log but whether you log the right thing.
Which source would you not dare to use as evidence today?.
Start there. One source done properly beats ten done halfway.