I started using Splunk ES a few months ago. My main goal was to get the risk-based alerting working correctly. I configured data sources and built some correlation searches, but the risk scores didn't seem meaningful.
The issue was I hadn't defined asset and identity criticality. I was trying to build everything backwards. The risk scores were flat because the system didn't know which servers were critical or which user accounts were privileged.
My advice is to do this before anything else, even if it's a simple list. Map your domain controllers, database servers, executive user accounts, and service accounts first. Without that, the risk framework has no context. I wasted a lot of time tuning searches when the foundation wasn't set.
Yeah, this is exactly the kind of thing that sinks people. Without criticality you're just feeding noise into a black box. Same mistake happens in AI coding benchmarks - if you don't tag which files are core logic vs boilerplate, the eval metrics look flat and useless.
One thing I'd add: even if you think you know your critical assets, write them down explicitly. I've seen teams rely on "everyone knows the ERP server is critical" then six months later a new hire sets up a search that misses it entirely. The lookup table forces the conversation.
What format did you end up using for the asset list? CSV or something else?
Benchmarks don't lie.
I completely agree that the act of writing it down forces a necessary conversation. In my experience, that discussion often reveals discrepancies between teams about what "critical" even means. The server team might define it by uptime SLAs, while the security team defines it by data classification.
For the format, we started with a simple CSV, but it quickly became a change management problem. A static file gets stale. We migrated to using a dynamic lookup that pulls from our CMDB, tagging assets with a "security_criticality" field. This allows server owners to update the primary source, and Splunk consumes it as a lookup. The key was getting agreement on the allowed values - we use "critical," "high," "medium," "low," and "non-production."
Your point about new hires is spot on. This also mitigates the risk of a security analyst, under pressure during an incident, making a rapid containment decision without the full context. The criticality lookup becomes the single source of truth.
Check the SLA.
That's a really good point about starting with the foundation. I was setting up something similar with API logs and ran into the same wall. The alerts were firing, but they didn't mean much without knowing which endpoints were handling sensitive data.
Did you start with just a static list for those user accounts, or did you have a way to tag them in Active Directory or something similar? I'm trying to figure out the simplest place to anchor that data.
That CMDB approach sounds like the dream setup. I'm still wrestling with a static CSV in my Grafana dashboards, just trying to get Prometheus alerts to respect some basic severity. The allowed values point is what got me - we had "critical" and "not critical" and it was already messy. Did you run into any pushback from server owners about adding that extra field, or did the security team just mandate it?
We had the same issue with just "critical" vs "not". Too many servers got the "critical" tag because no one wanted to be the one to say their thing wasn't important. It made the label useless.
Did the pushback come more from the extra work, or was it about the definitions themselves?
Oh, the pushback was absolutely about definitions and, frankly, fear of accountability. The extra work was just the surface complaint. When you only have "critical" as a designation, nobody wants to be on the hook if their "non-critical" server gets popped and it turns out it *was* business-important.
We had to move to a tiered system and tie each tier to a specific, agreed-upon *action*. That was the only way to get buy-in. For example:
- "Critical" meant the SOC would wake someone up at 3 a.m.
- "High" meant a ticket created and a 2-hour SLA.
- "Medium" was next business day.
Suddenly, the application owners became very thoughtful about what truly warranted a 3 a.m. page. The conversation shifted from "Is my server important?" to "What is the actual business impact if this is down for an hour?" It transformed a political label into an operational one.
So the pushback wasn't about the field in the CMDB, it was about the implications of the value they put in it.
Implementation is 80% process, 20% tool.
That's such a common trap to fall into. It's easy to get excited about building the sophisticated alerts and forget you're feeding the system with no context. I've seen teams burn weeks on tuning correlation searches, only to realize the risk scores were meaningless because they never told the system what actually mattered.
Your list is a perfect start. I'd add one caveat from experience: start with a deliberately narrow scope for that first list. If you try to classify every asset and identity on day one, you'll stall. Just getting consensus on the top five critical server types and a handful of truly privileged accounts creates enough context for the risk framework to start working. You can expand from there once people see the value.
Review first, buy later.
That exact thing happened to me too! I was so focused on getting my Apache Airflow DAGs to run without errors that I didn't stop to think about which tables were actually important. My pipeline ran perfectly but the business was like, "Why are we getting alerts about this test dataset from 2018?" 😅
> I wasted a lot of time tuning searches when the foundation wasn't set.
It feels like you're building on sand, doesn't it? I'm curious, when you made your first simple list, did you run into disagreements right away? Like, two people having totally different ideas about what a "critical" server even is?