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Detection,
At the Source

Run detection content the moment telemetry is collected — not after it's shipped, indexed, and paid for downstream.
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The Challenge

Detection engineering is stuck fighting the last mile — writing rules against whatever happens to land in the SIEM — when the real leverage sits upstream, at collection and normalization.

Detection Waits
on the SIEM

Rules only fire after events are shipped, parsed, and indexed downstream. That lag — often minutes to hours — is exactly the window attackers use to move laterally undetected.

Alert Fatigue From Noisy, Unstructured Data

Analysts drown in low-fidelity alerts because detections run against raw, inconsistent logs instead of clean, enriched events. Real signals get lost in the noise, and trust in alerts erodes.

Detection Logic Locked to One Schema

Rules written for one SIEM's field names and formats don't travel. Every migration, every added tool, means rewriting detection content from scratch instead of reusing what already works.

Coverage Gaps at the Edge

Remote sites, air-gapped networks, and high-volume sources often can't afford to ship everything centrally,
so detection simply doesn't run there — leaving exactly the blind spots attackers look for.

The Solution

AxoDetect brings detection engineering upstream — to the first mile of telemetry collection and normalization — so content runs the moment data is produced, not after it's been shipped and indexed.

Run Detection Where the Data Lives

Evaluate detection content — rules, ML models, threat intel lookups — directly in the pipeline, at the edge or in local storage, instead of waiting on a downstream index to catch up.

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One Detection Language, Any Destination

Write content once against normalized, schema-consistent data. Move SIEMs or add a new tool without rewriting a single detection rule.

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Federated Search Across Every Tier

Query across AxoStore and AxoLake from one workbench, correlating hot, warm, and cold data without rehydrating everything first.

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Full Coverage Visibility

See technique coverage against the MITRE ATT&CK matrix, alert volume, and pipeline health in one view — so gaps are visible instead of assumed.

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FAQs

What is detection engineering at the data layer?
What is detection engineering at the data layer?
It's running detection content — rules, models, enrichments — at the point telemetry is collected and normalized, instead of waiting until it reaches a central SIEM or log store.
Does this replace our SIEM's detection rules?
Does this replace our SIEM's detection rules?
No. It complements them. Detections still run in your SIEM; AxoDetect adds an earlier, faster layer that can catch threats before data ever ships downstream.
What kind of detection content can run in the pipeline?
What kind of detection content can run in the pipeline?
Static and behavioral rules, ML models, threat intel lookups, and other enrichments — anything that can evaluate a normalized event as it moves through the pipeline.
Does this reduce alert fatigue?
Does this reduce alert fatigue?
Yes. Running detections against clean, enriched, schema-consistent data instead of raw logs means alerts reflect real signal rather than parsing noise.
Can detection content move between SIEMs or tools?
Can detection content move between SIEMs or tools?
Yes. Because content runs against Axoflow's normalized schema rather than a vendor-specific one, the same detection logic travels with you through a migration or across a multi-tool environment.
Does this work for remote or air-gapped sites?
Does this work for remote or air-gapped sites?
Yes. Detection can run locally, right where data is produced, including in low-bandwidth or disconnected environments, without shipping everything to a central system first.

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Catch Threats Where Your Data Is Born, Not Where It Lands.

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