Security teams have never had more tools or more data problems. SIEMs, data lakes, threat intelligence platforms, observability tools, compliance archives, and AI engines all promise better outcomes, but only if they receive the right data. Most organizations treat their pipeline as a simple transport layer. They collect everything from everywhere, forward it to multiple destinations, and rely on each system to make sense of what it receives.
The result is predictable: Costs rise, data is duplicated, investigations slow, and AI, automation, analytics, and compliance workflows suffer from noisy or incomplete inputs.
CrowdStrike Falcon® Onum transforms that pipeline from passive infrastructure into an intelligent control plane. It filters, enriches, shapes, and routes telemetry in motion so every system receives high-fidelity, security-ready data.
For security leaders, these capabilities should shift their priorities from collecting more data to controlling how data moves, what context it carries, and where it delivers the most value. The following five use cases reflect common customer challenges and practical ways Falcon Onum helps teams take back control.
Modern environments generate staggering volumes of telemetry across endpoint, identity, network, cloud, SaaS, application, and infrastructure sources. The common approach is to ship everything into the SIEM “just in case.” Over time, ingestion costs balloon, storage grows, and search performance degrades. Analysts spend time digging through irrelevant events because data reduction happens after storage.
Falcon Onum changes that equation by enabling control upstream. Instead of treating data reduction as a downstream cleanup exercise, Falcon Onum shapes telemetry in motion. It drops repetitive health checks and known-benign noise, trims unnecessary fields before storage, and applies safe sampling and aggregation where appropriate. It also preserves high-value events and critical fields required for detection, investigation, and compliance.
Organizations can reduce unnecessary data volume before it reaches downstream systems, helping lower storage and processing overhead while preserving the visibility and investigative depth needed for security operations. One global telecom provider used Falcon Onum to turn high-volume network telemetry into real-time intelligence and faster operational response.
SIEM migrations rarely fail because of missing features. They stall because of time, risk, and integration complexity.
Falcon Onum introduces a control layer between sources and destinations to reduce friction. With this in place, organizations can decouple data producers from data consumers. With Falcon Onum, customers using CrowdStrike Falcon® Next-Gen SIEM and legacy solutions can run them in parallel without duplicating collection infrastructure. Data formats can be reshaped in motion without touching original source systems. Optimized streams can be delivered to analytics platforms while preserving full-fidelity feeds for detection. When storage tiers, retention strategies, or destinations change, source configurations remain intact.
As destinations evolve, the pipeline remains stable. This architectural flexibility reduces migration risk, shortens cutover timelines, and allows teams to modernize at their own pace while maintaining detection fidelity and investigative depth.
Falcon Next-Gen SIEM and the CrowdStrike Falcon® platform already deliver powerful security context through CrowdStrike threat intelligence, Falcon platform telemetry, asset context, adversary attribution, and detection workflows. This context is critical to high-fidelity detection and fast investigation.
But security operations rarely happen in one place. Data also flows to lakes, archives, observability platforms, third-party analytics tools, and operational systems. In those environments, telemetry often arrives with less context, different field structures, or no clear connection to the users, assets, applications, or locations involved. Analysts then spend time reconciling data across systems.
Falcon Onum helps add context while telemetry is still moving. Using supported enrichment patterns such as HTTP requests, Redis, static lookup files, parsing, field tagging, math expressions, and conditional logic, teams can enrich and shape events before they are stored or routed downstream. This allows each destination to receive telemetry that is more complete and better prepared for investigation, automation, and AI-driven workflows.
One practical example is an Impossible Traveler pipeline. Falcon Onum can parse VPN login events, send public IPs to a GeoIP service through an HTTP request, use Redis to retrieve the user’s prior login location, calculate the distance and time between logins, and determine whether the travel speed is suspicious. If the activity exceeds a defined threshold, Falcon Onum can build an alert and route it to a destination such as Slack, a SIEM, or another response workflow.
This kind of in-motion enrichment turns raw telemetry into actionable context before downstream systems have to process it. Analysts and AI agents spend less time interpreting raw logs because detection, investigation, and automation workflows start with better inputs.
In most enterprises, multiple teams depend on the same telemetry. Without centralized control over the pipeline, organizations respond by duplicating collectors, maintaining separate transformation layers, and building parallel integrations for each destination. Falcon Onum establishes a single, policy-driven pipeline that governs how telemetry flows across the ecosystem.
Data is processed once in motion and then routed intentionally based on defined conditions, metadata, content, or compliance requirements. A full-fidelity stream can be delivered to Falcon Next-Gen SIEM for detection and response. A search-optimized stream can be delivered to CrowdStrike Falcon LogScale™ for high-volume exploration. Long-tail telemetry can be archived to object storage for low-cost retention and deep historical queries. In one global telecom environment, this same approach helped ensure downstream systems received the telemetry they needed, structured and enriched for this specific operational purpose.
Routing decisions are deterministic and enforceable. Conditional logic can direct events based on severity, source, asset tags, tenant identifiers, or content inspection results. Sensitive fields can be masked or removed selectively without altering the original source stream. Each output is built for its destination rather than copied indiscriminately.
Investigations span multiple data homes, and the friction shows up when data is inconsistent across destinations. Federated Search is built for this. It allows analysts to query data where it already lives, across the Falcon platform and external data stores, so investigations can extend to archived and third-party telemetry without re-ingesting or duplicating it.
Falcon Onum sets up distributed investigations by shaping and tagging telemetry consistently as it moves through the pipeline. High-signal streams flow into Falcon Next Gen SIEM. Search-friendly copies can land in Falcon LogScale. Falcon Onum preserves consistent identifiers, tags, and key fields across every tier so pivots and correlations hold together across hot and cold data.
Federated access is governed through Falcon Next-Gen SIEM permissions, with query behavior dependent on the external system’s cost and performance characteristics.
In the Falcon Onum and Federated Search demo, this comes to life as an investigation flow where the analyst stays in Falcon Next Gen SIEM, pulls in archived context when needed, and keeps the same fields to pivot intact across hot and cold data. That is what makes distributed investigation feel cohesive.
Security outcomes depend on what enters the platform. When telemetry arrives noisy, incomplete, or late, every downstream system inherits the problem. Detections become less precise, investigations take longer, and automation becomes harder to trust.
Falcon Onum brings control forward in the process by applying lightweight decision logic while data is still in motion. Teams can filter known-benign events, suppress repetitive noise, tag high-priority activity, route suspicious events to the right destination, and preserve full-fidelity data where it is needed for investigation or compliance.
This does not replace detection and correlation in Falcon Next-Gen SIEM. It improves what reaches those systems. By shaping telemetry upstream, Falcon Onum helps Falcon Next-Gen SIEM, Falcon LogScale, AI workflows, analytics platforms, and compliance systems operate on higher-fidelity data that is fit for purpose.
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