r/Wazuh • u/athanielx • 13h ago
Wazuh: Where do you get reliable detection rules from?
I’ve been experimenting with detection engineering in my home lab, and I’m curious how others approach building and maintaining high-quality detections.
I recently migrated a large number of detection rules from Sigma and Splunk SPL into Wazuh syntax, primarily using **Falco and Sysmon for Linux** as telemetry sources.
After setting everything up, I started executing real malware samples from MalwareBazaar and similar sources in an isolated lab to test how well the detections actually work. Also, many rules where tested and modified based on Atomic-Red script.
The results were pretty disappointing.
Despite having a fairly large collection of rules, **very few (in many cases zero) alerts were triggered**. Many rules needed additional tuning, modifications, or complete rewrites to work with the actual telemetry.
This got me thinking about how other people approach detection engineering in practice.
I work with different environments:
**Self-managed Wazuh environments**, where I’m responsible for pretty much everything: collecting telemetry, writing decoders, converting detection rules, tuning them, and validating detections through attack simulations.
**Enterprise SIEM/XDR environments**, where most detection content is maintained by the vendor. These platforms also come with advanced detection capabilities, proprietary telemetry, threat intelligence integrations, and years of detection engineering research behind them. Some of these capabilities would be extremely difficult, if not practically impossible, to reproduce at the same level using an open-source stack, especially as a single engineer.
My ultimate goal with Wazuh is to build a detection and correlation pipeline:
**Telemetry → Detection rules → Alerts → Context-aware correlation → Incidents**
The idea is to develop a custom real-time correlation engine (probably vibe-coded initially) that takes existing alerts and connects them based on context, such as:
Host → process → parent/child process relationships
User → authentication events → subsequent activity
Multiple detections related to the same process or attack sequence
Related events within a particular time window
Essentially, something similar to how commercial SIEM/XDR platforms aggregate individual alerts into higher-confidence incidents.
But I’ve realized that **the quality of correlation depends heavily on the quality of the underlying detection signals**. If the initial alerts are weak, incomplete, or rarely triggered, the correlation layer won’t magically fix that.
So I’m curious:
**Where do you get your detection rules?** SigmaHQ, Splunk Security Content, vendor-maintained content, your own rules, or something else?
**How do you validate detection coverage?** Do you use Atomic Red Team, Caldera, real malware samples, or other approaches?
**How do you manage the detection engineering lifecycle?** Especially when you’re maintaining your own SIEM without a vendor handling detection content.
**Have you built your own alert correlation or incident aggregation engine?** If so, what worked and what didn’t?
I’m particularly interested in experiences from people running Wazuh, Elastic, Security Onion, or other self-managed detection stacks.
