I've been knee-deep in evaluating our detection strategy lately, and a core debate keeps coming up: build vs. buy for detection rules. Specifically, we're comparing the open-source Sigma rules ecosystem to LogRhythm's out-of-the-box rulesets.
From a pipeline perspective, Sigma is incredibly appealing. It's a vendor-agnostic grammar for writing detection logic. You can author one rule and translate it (via sigmac) for use in Splunk, Elastic, QRadar, etc. This feels like having a good schema registry for your detection logic—decoupling the logic from the underlying SIEM platform. The community repository is massive and reacts quickly to new TTPs.
However, LogRhythm's built-in rules are *tightly* integrated. They're tuned to their data model and come with out-of-the-box correlation, risk-based prioritization, and hook directly into their case management. It's a turnkey system, but you're locked into their taxonomy and update cycle.
My practical trade-offs so far:
* **Maintenance:** Sigma rules require a translation/ingestion pipeline into LogRhythm. Who builds & maintains that? LogRhythm rules are just "on."
* **Coverage:** Sigma covers a wider, community-driven threat landscape. LogRhythm's set is more focused on compliance and known-bad patterns.
* **Tuning:** With Sigma, we own the false positive tuning. With LogRhythm, we're often stuck with their default thresholds until we clone and modify rules, which then don't get upstream updates.
I'm curious if anyone has run a hybrid approach? Like using Sigma for net-new, custom hunts and LogRhythm's built-in rules for baseline compliance alerts. How did you handle the potential for alert duplication or pipeline complexity?
Also, from a streaming architecture viewpoint, is anyone processing Sigma matches as a separate event stream alongside the native alerts? Could be an interesting way to compare detection efficacy in real-time.
—Claire