Turn emerging threats into tested, deployable detections across your existing stack.
Native APIs · CrowdStrike · SentinelOne · Splunk · Sentinel
defenderlens://detection-ops
Traditional Problem · Authoring Velocity
Manual research and custom query writing takes ~5 days per threat technique.
Automated Detection-as-Code compilation generates validated rules from CTI in under 10 minutes.
Traditional Problem · Telemetry & Schemas
Schema mismatches and missing fields cause silent query failures across environments.
Telemetry requirements are mapped and validated before deployment, so detections fail closed—not silently.
Traditional Problem · Testing & Validation
Detections ship without structured testing, producing false positives and missed coverage.
Every detection is validated against requirements and expected behavior before it reaches production.
Traditional Problem · Multi-SIEM Deployment
Teams rewrite the same logic for Splunk, Sentinel, Falcon, and other platforms by hand.
One detection artifact compiles into platform-native syntax via native API integrations—no rip-and-replace.
Traditional Problem · Lifecycle Drift
As threats, telemetry, and environments change, detections decay without ongoing ownership.
DefenderLens keeps detections maintained across updates, schema shifts, and operational change.
DefenderLens connects the work between threat intelligence and operational detection.
DEFINE
Turn a threat, technique, behavior, or detection requirement into a structured detection objective.
Scope & IntelBUILD
Create detection logic in a structured, version-controlled workflow.
Git VersioningTEST
Validate detection logic and the requirements needed to support it.
Test PipelinesDEPLOY
Release validated detections to supported environments.
Native DeployMAINTAIN
Track changes and update detections as threats and environments evolve.
Continuous SyncDetection-as-code, testing, CI/CD, and lifecycle-based detection development are established industry practices
Gartner Research Report
“Gartner identifies detection-as-code and CI/CD automation as foundational pillars of modern, scalable detection engineering.”
Modern Security Operations Centers (SOCs) are abandoning manual console query entry in favor of version-controlled Git repositories, automated testing pipelines, and declarative detection definitions.
Forrester Wave Analysis
“Forrester defines detection engineering as a rigorous software lifecycle spanning ideation, design, build, test, release, and continuous monitoring.”
Continuous monitoring without automated testing and release management leads directly to high false-positive rates, alert fatigue, and silent detection decay as cloud log schemas shift.
SANS SOC Survey
“SANS research identifies time, fragmented telemetry, and cross-vendor data formatting as the primary friction points for detection teams.”
Detection engineers spend over 60% of their bandwidth wrangling schema mismatches, validating missing log fields, and manually translating queries across Splunk, Sentinel, and EDR consoles.
From initial threat intel ingestion to continuous schema validation and automated multi-SIEM deployment.
WORKS WITH YOUR STACK
Rules deploy in your platform's native syntax via native API integrations.
No rip-and-replace.
Deploy detection rules directly via native SentinelOne Singularity API
Deploy detection rules directly via native Falcon API integrations
Generate and deploy SPL rules to your Splunk environment
Deploy detection rules via native CrowdStrike NG-SIEM integrations
Deploy detection rules to Microsoft Defender XDR via native APIs
KQL detection rules and Sentinel ARM template deployments
