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Use Case · Data Center & Infrastructure AI

Real-time SIEM pipeline, built for production scale.

We design and build distributed SIEM pipelines that process high-volume network security events with sub-second detection latency. Not a SIEM vendor replacement — a production data layer that makes your security stack actually work at scale. Reference deployment: T-Mobile.

→ Why most SIEM implementations fail at scale

SIEM platforms are built for analysts, not for the data volumes that modern telco and data center environments generate. The failure mode is consistent: the event pipeline can't keep up with ingestion volume, detection latency grows from milliseconds to minutes, alert queues back up, analysts tune out the noise, and real threats slip through during the backlog.

Our reference engagement is T-Mobile, where we built the real-time SIEM pipeline handling distributed network security event processing at telco scale — sub-second threat detection latency across a high-volume, heterogeneous event stream. The problem wasn't the detection logic; it was the pipeline architecture that needed to handle the volume without degrading under load.

How we build production SIEM pipelines

1. Event ingestion and normalization

Kafka-based streaming ingestion from heterogeneous sources — firewalls, routers, hosts, cloud APIs, auth systems. Schema normalization into a unified event taxonomy before the detection layer. Malformed event handling and dead-letter queuing so pipeline failures are observable and recoverable.

2. Fast-path detection

Stateless rule matching applied at the stream layer, before any aggregation or correlation. Sub-100ms latency for high-confidence, single-event detections. Designed to handle the full event volume without backpressure.

3. Stateful anomaly detection

Windowed aggregations for behavioral anomaly detection — frequency analysis, sequence detection, threshold-based alerting across time windows. Kafka Streams or Flink depending on the state complexity and latency requirements.

4. Multi-source correlation

Event correlation across sources with bounded state and sub-second latency SLA. Join patterns that don't degrade under load. Priority queuing so complex correlations don't block fast-path detections.

5. Alert enrichment and routing

Context enrichment before alerts reach analysts — asset data, threat intelligence, historical context. Routing to existing SIEM, SOAR, or analyst tooling. Alert deduplication and grouping to reduce analyst fatigue.

[ Production benchmarks ]

What the pipeline needs to deliver.

Fast-path detection
<100ms
Stateless rule matching on the full event stream — no aggregation latency on high-confidence single-event detections
Anomaly detection
<1s
Windowed behavioral analysis — frequency, sequence, and threshold anomalies within a sub-second SLA
Pipeline resilience
Maintained under load
Backpressure handling, dead-letter queuing, and graceful degradation so detection latency holds during traffic spikes
[ FAQ ]

SIEM pipeline architecture — common questions.

What makes a SIEM pipeline 'production-grade' vs a proof of concept?
A production SIEM handles: irregular and malformed event formats from heterogeneous sources, backpressure and graceful degradation under traffic spikes, exactly-once or at-least-once delivery guarantees, schema evolution as new event types are added, and a detection latency SLA measured in milliseconds. A POC SIEM works on clean sample events in a single format. A production SIEM works on the full event firehose from hundreds of sources with no preprocessing guarantees.
How do you achieve sub-second detection latency at high event volume?
Sub-second detection requires: Kafka-based streaming ingestion with partitioning designed for the event volume, stateless detection rules applied at the stream layer (not batch), stateful anomaly detection using windowed aggregations in Flink or Kafka Streams, and a correlation layer that joins events across sources without unbounded state. The key design: separate the fast path (stateless rule matching, sub-100ms) from the slow path (multi-source correlation, sub-second) so simple detections don't wait on complex ones.
How does DehazeLabs integrate with existing SIEM vendors and tooling?
Most engagements layer on top of existing SIEM investments — Splunk, Microsoft Sentinel, CrowdStrike, or custom tooling. We don't replace the analyst workflow; we fix the data pipeline that feeds it. Common pattern: normalize and enrich events before they hit the SIEM so rule-matching is faster and alert fatigue is lower. Where existing SIEMs can't handle the event volume or latency requirements, we build the streaming layer that handles volume and uses the SIEM for correlation and analyst tooling.

Security event pipeline that needs to work at your scale?

Tell us your event volume, source diversity, and current detection latency. We'll tell you where the architecture needs to change to get to sub-second at production scale.

Schedule a conversation → Data Center & Infra AI