Hey everyone! 👋 I've been knee-deep in Splunk ES for the better part of a year now, trying to wrangle our security monitoring into shape. The biggest hurdle we keep hitting, and I'm sure many of you can relate, is **alert fatigue** from a constant barrage of low-fidelity signals. You know the ones—the "possible" this, the "anomalous" that, which 99 times out of 100 turn out to be a benign configuration quirk or expected network noise.
It's drowning my analysts, and we're worried about missing the real threats in the sea of false positives. I've been experimenting with tuning the existing correlation searches and risk-based alerting, but I'd love to compare notes with the community.
Here’s a bit of what I’ve tried so far, focusing on making alerts more actionable:
* **Aggressive Risk Threshold Adjustment:** I started by significantly raising the risk thresholds for risk-based alerts in the Enterprise Security Config UI. Instead of firing on a risk score of 20, we might set it to 50 for certain object types. This helped, but felt a bit blunt.
* **Tuning Correlation Searches with Suppression:** This has been the most effective step. For example, we have a noisy correlation search for "Multiple Failed Logins." I added suppression logic to ignore failures from our automated system accounts and specific, known noisy IP ranges (like our VPN concentrators).
```xml
user="svc-*"
src_ip="10.10.10.0/24"
```
* **Leveraging Adaptive Response Actions:** For some medium-fidelity alerts, we switched from generating a notable event to a less intrusive action, like simply adding a risk entry. This lets us accumulate evidence for an entity without creating analyst fatigue on every single event.
* **Contextual Enrichment is Key:** I found that many alerts become "low-fidelity" because they lack context. We've been working on enriching our asset and identity data massively. An alert from an unknown device is noisy; an alert from a CFO's laptop tagged as "high-value asset" is immediately high-fidelity.
My current puzzle is figuring out the most sustainable way to **proactively score and deprioritize certain data sources or event types** that are historically noisy *for our environment*. Is the best path through creating custom risk rules that negatively score these frequent false positives? Or should I be looking more at data model acceleration filters?
What are your go-to recipes for cutting down the noise? Have you built any cool workflows with Zapier or Make to auto-ticket only the high-fidelity alerts, or to funnel low-fidelity ones into a separate review queue? I'm all ears for your gotchas and success stories!
-- Ian
Integration Ian