Detection engineering

Detection Engineering Services

Detection engineering focused on real attacker behavior - building reliable, low-noise detections for Wazuh and Splunk that analysts can trust.

  • Build detections tied to real attacker behavior
  • Reduce noise and false positives
  • Detections that analysts can trust and act on

What you get on day one

Concise scope, test plan, and outcomes your team can execute.

Wazuh & Splunk

Platforms

Aligned to your environment.

Actionable alerts

Primary outcome

Not raw events.

Tuned

Noise reduction

Validated through testing.

1–4 weeks

Timeline

Depends on scope.

OWASP ASVSCWENIST 800-53ISO 27001

Why detection engineering

Why detection engineering

Good detections are built, not installed

Effective detection requires understanding behavior, data, and operations.

Alerts fail when they aren’t grounded in behavior

Generic rules trigger constantly and still miss real attacks. Detections need to reflect how attackers actually operate.

Noise erodes trust

When analysts are flooded with alerts, real incidents get ignored or delayed.

Detections must match the environment

Rules need to reflect your logging sources, operating systems, and tooling - not a vendor demo dataset.

What we build

What we build

Detections that fit your environment

Focused on behavior, signal quality, and analyst usability.

Behavior-based detections

Detections mapped to attacker techniques, not isolated log patterns.

Log-source aware logic

Rules designed specifically for the data your Wazuh and Splunk instances actually ingest.

Noise controls

Thresholds, suppressions, and context checks to reduce false positives.

Cross-signal correlation

Combine multiple weak signals into a single high-confidence alert.

Detection validation

Test detections against known attack scenarios and benign activity.

Documentation and handoff

Clear explanation of what each detection means and how analysts should respond.

How we work

How we work

From idea to usable detection

A practical process that balances security and operations.

Threat and detection goals

Align on what threats matter most to your environment and business.

Log and telemetry review

Confirm what data is available, reliable, and usable for detection.

Detection design

Write detection logic tailored to Wazuh and/or Splunk data models.

Tuning and validation

Reduce noise and validate behavior against real scenarios.

Analyst handoff

Deliver detections with context, severity guidance, and response notes.

Designed for analysts

We design detections with investigation time in mind, not just theoretical coverage.

Deliverables

Deliverables

Production-ready detections and documentation

Clear output your SOC can operate and maintain.

Detection rules and queries

Production-ready detections implemented in Wazuh rules and Splunk searches.

Tuning and suppression guidance

Clear notes on thresholds, exclusions, and expected behavior.

Detection documentation

What the alert means, why it fires, and how to investigate.

Validation notes

Evidence showing detections were tested against attack and benign activity.

Ready when you are

Build detections your team can trust

We’ll help you create reliable, low-noise detections aligned to real attacker behavior.

Engagement options

Engagement options

Build or improve detections

Start fresh or fix what isn’t working.

Detection Build-Out

Create a core set of high-confidence detections for your SOC.

  • Threat-aligned detection set
  • Noise reduction and tuning
  • Analyst-ready documentation

Detection Improvement

Fix noisy or ineffective detections in an existing environment.

  • Rule review and cleanup
  • False-positive reduction
  • Validation and re-testing

FAQ

FAQ

Before we start

Do you only work with Wazuh and Splunk?

These are our primary platforms, but the detection logic is transferable to other SIEMs if needed.

Do you provide SOC operations?

No. This service focuses on detection engineering, not day-to-day SOC operations.

How do you validate detections?

We test detections against known attack behavior and expected benign activity where possible.

Will this reduce alert volume?

Yes. A key goal is fewer, higher-confidence alerts that analysts can trust.