Detection Engineering Services
Detection engineering focused on real attacker behavior — building reliable, low-noise detections for Wazuh and Splunk 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.
Why detection engineering
Good detections are built, not installed
Effective detection requires understanding attacker behavior, your data, and your operations. Generic rule sets create noise — purpose-built detections create actionable signals.
Alerts fail without behavioral grounding
Generic rules trigger on log patterns that look suspicious but aren't. Effective detections need to reflect how real attackers operate in your specific environment — not a vendor's demo dataset.
Noise erodes analyst trust
When analysts are flooded with low-quality alerts, real incidents get ignored, delayed, or lost in triage queues. False-positive reduction is not optional — it's the difference between a working SOC and theater.
Detections must match your data
Rules designed for one SIEM's data model break or go silent on another. We build detections specifically for the log sources, field mappings, and data quality your Wazuh and Splunk instances actually have.
Coverage without context is noise
Mapping to MITRE ATT&CK is a starting point, not a goal. Each detection needs expected analyst actions, severity context, and triage guidance — otherwise it's just another alert in the queue.
What we build
Detections that fit your environment
Focused on attacker behavior, signal quality, and analyst usability — built specifically for your Wazuh and Splunk data.
Behavior-based detections
Detections mapped to attacker techniques — process injection, credential dumping, lateral movement, persistence, and evasion. We focus on behaviors that matter to your threat model, not isolated log events.
Log-source aware logic
Rules designed specifically for the data your Wazuh and Splunk instances actually ingest — correct field names, proper parsing, and tested against your real log volume. No generic rules that assume data you don't have.
Noise controls
Thresholds, suppressions, allowlists, and context enrichment to reduce false positives. Each rule ships with tuning notes documenting expected noise sources and recommended exclusion patterns.
Cross-signal correlation
Combine multiple weak signals into single high-confidence alerts. A failed login alone means nothing — a failed login followed by a successful one from a new IP with privilege use is actionable.
Detection validation
Test every detection against known attack scenarios (atomic red team, manual simulation) and expected benign activity. We record results so your team can repeat validation after environment changes.
Documentation & handoff
Clear explanation of what each detection catches, why it fires, expected false positive sources, and exactly how analysts should investigate and respond. Designed for operational use, not a PDF.
How we work
From threat model to usable detection
A practical process that balances security coverage with operational noise tolerance.
Threat & detection goals
Align on which threats matter most to your environment and business. Map attacker techniques relevant to your industry, infrastructure, and existing visibility.
Log & telemetry review
Audit what data is available, reliably forwarded, and correctly parsed. Identify gaps that would make detections unreliable or create blind spots in coverage.
Detection design
Write detection logic tailored to Wazuh rules and/or Splunk searches against your actual data models. Each rule includes severity, context, and expected analyst actions.
Tuning & validation
Test detections against attack simulations and real benign traffic. Tune thresholds, add suppressions, and document expected noise sources until alert quality meets operational standards.
Analyst handoff
Deliver detections with full documentation — what it catches, why it fires, investigation steps, escalation criteria, and maintenance notes. Walk through scenarios with your team.
Designed for analysts, not compliance archives
We design detections with investigation time in mind. Every rule includes what to look for, what to ignore, and when to escalate — so analysts spend time investigating, not guessing.
Deliverables
Production-ready detections and documentation
Clear output your SOC can operate and maintain — from rules and queries through tuning notes to analyst investigation guides.
Detection rules & queries
Production-ready detections implemented directly in your Wazuh rules and Splunk saved searches — tested against real data and ready for immediate use.
- Wazuh XML rules or Splunk SPL
- MITRE ATT&CK technique mapping
- Tested against live data
Tuning documentation
Clear notes on thresholds, exclusions, expected benign sources, and suppression patterns for each detection — so your team can tune confidently after handoff.
- False positive sources documented
- Threshold and suppression guidance
- Allowlist recommendations
Detection documentation
What each alert means, why it fires, how to investigate, and when to escalate. Written for analysts working under pressure, not for compliance archives.
- Investigation steps per alert
- Severity and escalation criteria
- Related detections and context
Validation evidence
Test results showing detections fired correctly against attack simulations and didn't false-positive against expected benign activity. Reproducible for future re-validation.
- Attack simulation results
- Benign traffic test results
- Reproducible test procedures
Ready when you are
Build detections your team can trust
We'll help you create reliable, low-noise detections aligned to real attacker behavior for Wazuh and Splunk.
Engagement options
Build or improve detections
Start fresh with a threat-aligned detection set, or fix what isn't working in your existing environment.
Detection build-out
Create a core set of high-confidence detections for your SOC — behavior-based, environment-specific, and tuned for low noise. Best for teams standing up detection capability or expanding coverage into new threat areas.
- Threat-aligned detection rule set
- Noise reduction and false-positive tuning
- Full analyst documentation and handoff
Detection improvement
Fix noisy, broken, or ineffective detections in an existing Wazuh or Splunk environment. Audit what exists, clean up what's failing, and fill gaps in coverage with tested replacements.
- Existing rule review and cleanup
- False-positive reduction and re-tuning
- Validation testing and updated documentation
FAQ
What teams ask before we start
Do you only work with Wazuh and Splunk?+
Wazuh and Splunk are our primary platforms where we deliver production-ready rules. The detection logic and methodology are transferable to other SIEMs (Elastic, Sentinel, Chronicle) if needed — we can deliver pseudocode and guidance for other platforms.
Do you provide SOC operations?+
No. This service focuses on detection engineering — building, tuning, and documenting the detections. For full SOC enablement including response workflows and operational documentation, see our SOC & SIEM Enablement service.
How do you validate detections?+
We test detections against known attack behavior using atomic red team tests, manual simulation, and environment-specific scenarios. We also test against expected benign activity to verify acceptable false-positive rates before handoff.
Will this reduce our alert volume?+
Yes. A primary goal is fewer, higher-confidence alerts that analysts can actually trust and investigate. We replace noisy generic rules with tuned, behavior-based detections and document expected noise sources for ongoing maintenance.
How do detections stay current?+
We deliver detections with tuning documentation and maintenance guidance. For ongoing coverage, we recommend periodic detection reviews — especially after red team engagements, threat model updates, or significant infrastructure changes.
Can you build detections from red team findings?+
Yes. Red team engagement reports are excellent input for detection engineering. We can take specific TTPs observed during red team assessments and build targeted detections to catch that exact behavior in your environment.