Threat Detection Engineering

Fewer alerts. Better detection.

We review, tune and build the detections your SOC depends on across platforms including Microsoft Sentinel, Splunk, Google SecOps and Defender XDR, or the SIEM, EDR and other security tooling your team already uses.

Discuss your detections

The problem

Detection content builds up faster than anyone reviews it. Rules get switched on from vendor templates, exceptions get added in a hurry, and nobody goes back to check what still works. Analysts learn which alerts are usually noise, increasing the chance that a real threat gets missed.

This is for you if

Analysts spend too much time closing false positives.
You have hundreds of detections but aren't confident they're the right ones.
Rules have built up without regular tuning or clear ownership.
You're migrating SIEM or EDR platforms and don't want to lift and shift old detections.
Your team doesn't have time to build and maintain detection content.

What we do

We treat detections as engineering work: each one has a purpose, an owner and a way to test it.

Review existing rules for alert volume, accuracy and the usefulness of what they surface.
Tune noisy rules, scope exclusions carefully and retire rules that no longer add value.
Add context to alerts so analysts can make decisions with fewer manual pivots across different tools.
Map existing coverage to MITRE ATT&CK and prioritise gaps based on the threats most relevant to your organisation.
Build and test new detections in KQL, SPL or the query language used by your platform.
Document each detection's logic, expected triggers and response guidance.
For teams that want a more mature engineering workflow, move detection content into version control (detection-as-code) so changes can be reviewed and tested.
Carry detection content across SIEM or EDR migrations, rebuilding what's worth keeping rather than copying it across.

What you get

Fewer false positives and less analyst time lost to low-value alerts.
New production-ready detections in the relevant SIEM or EDR tooling.
Documented detection logic your team can maintain.
A before-and-after view of alert volume and detection coverage.

Results

32%reduction in alert volume across a 30+ analyst MSSP SOCStructured detection tuning and analyst feedback reduced unnecessary alert volume.Read the case study

A good place to start

Noisy rules or coverage gaps?

Detection Engineering Sprint

typically 2-4 weeks

We review noisy detections, false positives and alerts that aren’t giving analysts enough useful information to act on. We tune or retire what isn’t working, identify coverage gaps and build the priority detections needed to address them.

You get: tuned and retired rules, new production-ready detections in the relevant source tooling (e.g. SIEM, EDR), documented detection logic, and a before-and-after view of alert volume and detection coverage.

Discuss a Detection Engineering Sprint

Migrating SIEM or EDR?

Talk to us about moving your detection content to the new platform, keeping what works and rebuilding what doesn't.

Discuss your migration

Platforms and frameworks

Our experience includes Microsoft Sentinel, Microsoft Defender XDR, Splunk and Google SecOps, alongside the MITRE ATT&CK framework. We work with the tooling already in your environment rather than pushing a preferred platform.

Frequently Asked Questions

What is detection engineering?

Detection engineering is the work of designing, building, testing and maintaining the rules that raise alerts in your SIEM and EDR. It treats detections like software: each one has a clear purpose, documented logic and a way to confirm it still works. Vendor rules out of the box are a starting point, not a finished detection capability.

How do you reduce false positives without missing real attacks?

By understanding why a rule fires before changing it. We look at what triggered each noisy alert, then narrow the logic or add exclusions scoped to the specific benign activity, rather than switching the rule off. Where a rule can't be made reliable, we either retire it or replace it with a better detection for the same behaviour.

Do you only work with Microsoft Sentinel?

No. We have deep experience with Sentinel and Defender XDR, where detections are commonly written in KQL, and we also work with Splunk, Google SecOps and other SIEM and EDR platforms. The underlying detection engineering principles carry across platforms, although the query language, data model and detection capabilities differ.

Tell us what you're working on.

Whether it's a noisy SOC, detections you don't trust, an untested response plan or a security project that needs delivering, a short call is the quickest way to work out what to do next.

Talk to a security specialist

Free 30-minute call · No obligation · Speak directly with the person who'd do the work