Having worked extensively with both Splunk ES and Datadog's security offerings, I often see teams struggle with the cumulative cost and performance impact of unmanaged data ingestion in Splunk. A primary lever for controlling this is systematically pruning unnecessary data sources from your Enterprise Security indexes. This is not merely about cost savings; it directly impacts search performance and the manageability of your security operations.
The process begins with identifying data that provides negligible security value. Common candidates include verbose application debug logs ingested for troubleshooting but never referenced in correlations, overly granular network flow data beyond what your use cases require, and redundant system telemetry already covered by more specific security logs. In Splunk, you can use the `tstats` command to profile index usage by sourcetype over a significant period, such as 30 days.
```sql
| tstats count WHERE index=*_security BY index, sourcetype
| search count < 1000
| sort count
```
This query helps pinpoint low-volume sourcetypes, which are often prime candidates for review. However, volume alone isn't the only factor; you must validate that the data is not used in any critical ES correlation searches or risk analyses. Cross-reference these sourcetypes against your notable event adapters and asset/identity lookup configurations.
The actual pruning is a governance exercise. For each candidate data source, document its intended security purpose and verify its absence from active use cases. Then, implement filtering at the forwarder level using `props.conf` and `transforms.conf` to drop the unneeded events, or reconfigure the data collection to cease entirely. Always stage this in a development environment first and monitor for any unintended gaps in your security posture. The goal is to maintain detection coverage while eliminating waste, a principle that is equally central to effective observability platforms.
null