The Security Data Lake Replaces the SIEM's Storage (Without Ripping Out Your SIEM)

A full SIEM replacement is close to a year of work before a single new query runs: parser rewrites, detection-rule migration, dashboard rebuilds, and a parallel-run period nobody enjoys. Most teams do not have that year. They also cannot keep routing every log into a platform priced by the gigabyte ingested. A 100 GB/day Splunk deployment lists around $76,000 a year for the base platform before Enterprise Security is added, and the security add-on roughly doubles that.

Replacing a SIEM outright is usually a slower and riskier path. The industry isn’t short on stories about unstructured rollouts, where teams try to onboard every source at once, produce long, complex implementations, cost overruns, and more. A cutover means rewriting parsers for every source, migrating detection logic by hand, and re-tuning correlation content your team built over years, all while running two systems in parallel and hoping no detection gap opens during the switch. The lower-risk path is to stop feeding it the data it charges the most to hold, and to do that one source at a time.

Between ripping the SIEM out and leaving it to run up the bill, there is a third option that most teams skip: leave the SIEM in place and change what it has to carry.

What Layering a Data Lake Under Your SIEM Actually Looks Like

You stand up a normalized data lake beneath the SIEM you already run and route data by signal value. High-volume, low-signal sources such as firewall, DNS, flow, and endpoint telemetry land in the lake, where they are normalized to OCSF, deduplicated, and enriched with intelligence at ingest. The SIEM keeps the high-value detection content and the correlation your team spent years tuning.

The routing math is usually lopsided in your favor. DNS and flow logs can account for a large share of ingest volume while driving a small share of your active detections. Moving them to the lake stops the SIEM from charging you to store data it barely uses for detection, and the data stays fully searchable next to your hot SIEM records. The SIEM stops being the aggregation point it was never architected to be.

How OCSF Makes the Routing Durable

OCSF decouples your data model from any single vendor, so lake data stays queryable alongside hot SIEM data instead of becoming a pile of records nobody can correlate. One shared schema means a field like a blocked connection is represented the same way whether it came from a firewall, a proxy, or a cloud control, so a detection written once matches everywhere instead of being rewritten per source.

The standard is well past experimental. OCSF joined the Linux Foundation in November 2024, and its community lists more than 900 contributors and roughly 200 participating organizations, including security vendors, government agencies, and enterprises. The International Telecommunication Union has backed OCSF for ratification as an international standard, which matters for teams in regulated and public-sector environments that anchor procurement to recognized standards.

Migration and Augmentation Without a Cutover Weekend

Run the migration as a parallel operation using the strangler-fig pattern: stand the new layer up beside the old system, move one data source and one detection at a time, confirm parity, and retire a SIEM ingest path only once the lake reliably serves it. Move DNS logs first, confirm that the DNS-based detections fire identically off lake data, then cut the SIEM's DNS feed and book the savings. Repeat by source. There is no cutover weekend and no window where a detection stops running. Every source you move lowers the SIEM bill and widens the enriched dataset your agents will later reason over.

Augmentation is a complete first move that stands on its own economics, and it runs on the same platform you would use at full replacement, on a timeline you set. You enter at storage and enrichment, prove the cost and detection case on real data, and expand when you are ready. Nothing about starting underneath your stack commits you to a rip-and-replace you have not decided on. The enriched, normalized dataset you build in the process is also the foundation any future agentic operation depends on, so the first move and the eventual destination are the same architecture at different stages.

What This Means Across the Enterprise

The value lands differently depending on who’s reading the proposal:

- Security architect: no forklift and no parser-rewrite marathon. You add capability under the stack you already run and control the sequencing yourself.

- CISO: the SIEM bill drops with every source you move off ingest-based pricing, and the savings are built from your own volume numbers rather than a vendor benchmark.

- SOC director: noise is deduplicated and enriched before it becomes an alert, so the queue that reaches analysts is smaller and arrives already attributed.

- CTI lead: the feeds, confidence thresholds, and curation standards you maintain become the intelligence fused into every event at ingest, not a lookup performed after an alert fires.

How to Build the Business Case

Start by building it on your own numbers, not a vendor's headline stat: ingest volume reduced, retention cost avoided, and analyst hours recovered from noise, with augmentation measured against the cost of full replacement. The three inputs are usually enough to make the case on their own.

Find out more about how high-fidelity data helps build a successful agentic SOC.