Managing SOC data effectively with Splunk software
Managing massive data volumes is a critical strategy for SOC teams. Proper data management will also boost search performance and workload capacity, as well as allow you to find and fix any issue faster and break down silos of security and observability data for richer context. However, the Splunk State of Security Report 2025 shows that:
- 66% of organizations experienced a data breach in 2024
- 46% of SOC teams spend more time managing tools than doing threat investigation
- 32% of SOC teams do not have the right skills sets to be effective
If these pain points are familiar to you, read on. This article provides three interconnected strategies for leveraging the Splunk platform to get more value from your data.
How to use Splunk software for this use case
Data tiering
Not all data is created equal. There are varying levels of priority and criticality, and so to manage it all the same way doesn't make sense. Ingesting verbose logs into the Splunk platform will be extremely costly, but losing context for certain logs - like compliance-related logs - can violate the law. Compliance logs need the unaltered raw data and digital signatures, while logs for incident investigations might need enrichment.
Therefore, a good first step is to determine the purpose of each data source and then manage how you store that data appropriately.
| Performance tier | Data use case | Data age | Technology | Cost profile |
|---|---|---|---|---|
| High performance | Real-time detection, prevention, monitoring | <1 sec to <1 min | Splunk indexes (hot/warm) | High cost |
| Performance at cost | Rare incident investigations, threat hunting | 1 hour to 1 month | Federated analytics | Medium cost |
| Compliance | Forensics, audit, long-term retention | 1 year to forever | Federated search | Low cost (S3 archive) |
Want to learn more about data tiering strategies? Check out these Splunk Lantern articles:
Data management (DMX)
A common flow for collecting security data goes follows this path: collection > parsing > normalization > enrichment. But as you just read, you want to employ a data tiering strategy, which means you shouldn't be simply collecting everything. You need to filter the data before collection and route it correctly, according to the tiering guidelines above.
The Data Management Experience in the Splunk platform takes care of these extra steps. It is delivered through a single, modern UI experience. It is offered through two deployment methods:
- Splunk Ingest Processor as a Splunk-hosted SaaS offering, great for customers who are all-in on cloud
- Splunk Edge Processor as a customer-hosted offering, great if you want more control at the edge
In one example of filtering with Edge Processor, if you have a firewall that generates 100GB/day of logs, Splunk Edge Processor filtered that volume to 20GB/day, an 80% reduction. It routed the full 100GB to S3 for compliance and 20GB to the Splunk platform for real-time detection. What is filtered to S3 does not count toward your license usage, ultimately saving a lot of money. In addition, Edge Processor provides data masking capabilities to help you maintain compliance with regulations such as GDPR.
Ready to learn more? The Transform and Optimize Pipelines content library on Splunk Lantern has lots of articles that will help you use Edge Processor, Ingest Processor, and Ingest Actions.
In addition, AI enhancements to DMX capabilities are coming. SOC data pipelines are fragile, and with the volume of data you collect, you might not even notice that you've lost some percentage of your data. The following table outlines some problems you might be familiar with.
| Problem | What happens | Impact on the SOC |
|---|---|---|
| Schema drift | Source adds/removes fields, changes data types (e.g., string → int, or new JSON nested field) | Pipeline silently fails or corrupts data → blank dashboards, missed threats, hours of debugging |
| Parsing failures | Timestamp format changes, multiline logs misbreak, encoding issues | Events missing from Splunk → blind spots in detection, compliance gaps |
| Destination issues | Indexer queues full, S3 bucket permissions change, network blips | Data loss, backpressure, manual reprocessing |
| Volume spikes | Log verbosity increases 10x unexpectedly | Indexing delays, license overages, SOC alert fatigue |
The AI-enabled self-healing pipelines in DMX will do the following:
- Detect: AI monitors pipelines in real-time for drift, parsing errors, volume anomalies
- Diagnose: Auto-identifies root cause (e.g., "Firewall logs added 'new_field' column")
- Heal: Applies fallback logic:
- Quarantines bad data
- Auto-adjusts parsing rules
- Reroutes to alternate destination (S3 fallback)
- Generates fallback SPL2 transformations
- Alert: Only notifies humans if self-healing fails
Want to learn more about AI-driven capabilities for self-healing in your data pipeline? See the Splunk YouTube video Maximizing Data Value in the AI Era with Splunk Platform.
Federation (S3)
So far, you've read how routing data to S3 can save you money as part of a solid data tiering strategy, but typical multinational companies have another reason for using S3. They often have multiple SIEMs and security repositories that are continent or branch-based. To remain in compliance, they cannot move the data between repositories, but they still need to search it all from a centralized location and ensure it is all governed by consistent policies. This is where federated search becomes helpful.
Federated Search for Amazon S3 is a search capability that allows you to perform a remote search on your Amazon S3 buckets and retrieve the search results directly in your Splunk Cloud Platform instance for correlation, enrichment and analysis. Federated search allows you to mix and match cloud and on-premises data. Note that while you can directly query data in Amazon S3, it is slow, so you only want to do this for compliance reasons.
Want to learn more about how to use federated search? Check out these Splunk Lantern articles:
- Accelerating security forensics with Federated Search for Amazon S3
- Simplifying compliance trails and audits with Federated Search for Amazon S3
- Correlating data for threat insights using Federated Search for Amazon S3
- Performing data exploration and statistical analysis with Federated Search for Amazon S3
Next steps
Now that you have an idea of how to manage your data more effectively, watch the full talk from Cisco Live EMEA 2026, Data Management and Federation: Manage SOC Data Your Way with Splunk. In the talk, you'll learn more about each of these topics, including special considerations for compliance logs and how federated search works under the hood.