Introduction
Having spent years in the trenches of a SOC, I’ve seen a massive gap that consistently gets overlooked. It’s the void between getting the data and making it useful.
The Void
The void is the no man’s land between the source and the destination where ownership completely disappears.
Vendors have essentially done their job. They’ve got data into your estate, and they don’t care what you do with it after that. They have fulfilled the requirement to receive logs for monitoring.
On the other end, we essentially have a bucket. It’s designed to receive, not to police. Most platforms don’t enforce a unified schema because it isn’t worth their time. They would have to write a parser for every single application schema out there.
Beyond the technical hurdle, there’s a commercial one. Most of these platforms have a vested interest in you ingesting everything. They want the volume. They don’t care if 40% of it is unformatted noise, because they’re getting paid for the storage either way. It doesn’t make sense for them to help you filter or standardise at the ingestion layer when doing so is both technically challenging and commercially unattractive for them.
This leaves you with two partners who have both fulfilled their contracts while leaving you with a pile of unusable data. Most teams start their engineering process after ingestion by the SIEM. By the time the data is in the bucket, the damage is done. You’re already paying for the mess, and your engineers are already playing catch up. You are building your entire security estate on a foundation of unorganised data.
Navigating the Void
If the vendor won’t own the data, and the SIEM can’t police it, someone has to. That someone shouldn’t be your engineers doing it by hand, throwing hours at a problem that is fundamentally architectural.
The solution is to unify the schemas pre-ingestion. Instead of letting un-unified data straight into your SIEM, route it through a dedicated translation engine before it ever touches your estate.
We help companies navigate the void using the following approach:
- Enforced normalisation: The moment a log leaves the vendor, we catch it. We strip the vendor-specific schema and translate it into a universal schema. By the time it reaches your SIEM, the void has already been closed.
- Schema on ingestion: Because your data is already unified, your dashboards, threat hunts, and detections work instantly.
- Strategic log selection: Because we sit in the middle, what we give you is essentially an off switch. You can drop all the noise that adds zero security value.
The Result
You own the architecture again. We take the responsibility that the vendors dropped and the platforms ignored, and we automate it.
You move from a world where you are reacting to your data, to a world where you fully control it. It saves so much time and effort. If this one thing gets done correctly, everything else becomes easier.
How to Close the Void Today
When people think of architectural problems, they normally picture a long timeframe: a big go-live where everyone rushes to get everything right before the switch-over. This one doesn’t have to work that way. It can be done fractionally, with a low barrier to entry. You can send everything, or you can start with a single log stream.
Log selection can be precise:
- You can censor specific keywords.
- You can drop logs that carry zero detection value.
- You can set granular routing rules based on log content, origin, or timing.
You don’t have to turn off your current SIEM or move your data lakes. You simply point your data sources to the pipeline first. We sit in front of your existing tools. Your current architecture stays exactly as it is. It just starts receiving better data.
Because our schema is already mapped to global standards, you don’t have to spend weeks learning how to format your logs. You turn it on, select your destination, and the data arrives pre-cleaned, pre-formatted, and ready for your existing alerts to consume.
You can look at your raw feed and identify the logs that are bloating your bill. With one click, you drop the noise at the ingestion layer. It is not just about data quality: you are paying for the change through the storage costs you save downstream.
You can start with one data source. Pick your messiest, most expensive, or most fragile log source. Route it through the pipeline. Watch your engineering hours for that source drop to near zero. Then move to the next.
Final Thoughts
Closing the void cleans up your logs, but the effect runs further than that. Enforcing a unified schema at the point of ingestion means automation finally has predictable data to work with, and AI or analytics projects have a real foundation to build on instead of a mess to clean up first.
If you’re ready to close the void, let’s talk. We’ll walk you through exactly how it works on your own data.