In cybersecurity, value no longer lies only in detection, but in ingestion. The rise in power of data -oriented architectures brings about a new issue: regain control over flows, even before they reach the analysis platforms. In this context, Data pipelines As Cribable Stream becomes strategic bricks, capable of optimizing the performance of SIEMs, reducing operating costs and redefining the agility of Secops teams.
A volume of data which has become critical
Each year, the volume of machine data – logs, metrics, events, traces – grows by 28 % on average. At the same time, IT budgets stagnate. Result: security architectures, especially the SIEMs, struggle to keep the pace. These platforms, often priced in ingestion, are overloaded with redundant data, poorly formatted or unused by detection rules.
The challenge is no longer simply to detect threats, but to select What data deserves to be analyzed, stored or excluded. Upstream of the SIEM, it is the ingesting infrastructure which becomes the first lever for performance and cost control.
The end of “send everything”
Historically, architectures collected mass logs, with specific agents for each target platform. Each tool (SIEM, observability, Data Warehouse) deployed its own pipeline, generating a duplication of data, increasing complexity and structural additional cost.
Today, this model reaches its limits. The answer provided by new platforms as CRIBL, Datadog, Fluentd is based on a simple principle: centralize collection, sorting in real time, routing intelligently.
CRIBL STREAM acts as universal collector : it captures the raw data, prepares it (parsing, filtering, enrichment), optimizing it (deletion of redundancies, conversion into metrics), then redirects them according to their usefulness to the right destination: SIEM, data lake, analysis engine.
Data optimization becomes a budgetary imperative
One of the major contributions of this type of solution is economical. By only transmitting to SIEMs the data actually exploitable for correlation rules and investigations, companies reduce:
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- THE Volumes ingested (up to –50 %)
- THE license costs
- THE Premium storage needs
- There Platform calculation load
Some customers cited by CRIBL thus brought back their storage needs from 136 TB to 2 TB for legal retention. Others have reduced their ingestion bill to 1.7 million to $ 400,000 per year by tilting part of the data to low-cost supports (S3, Azure Blob, Cribl Lake).
Towards a decoupled and reversible architecture
The other asset of these smart pipelinesthis is their ability to decouple the sources of destinations. A company can, from the same gross data, send it simultaneously:
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- To an SIEM for immediate detection
- To a data lake for long -term conservation
- To a BI base for business analyzes
This approach also makes it possible to test new tools or to operate Progressive SIEM migrationswithout dependence on the native pipeline of the existing solution. Migration projects, often estimated at 18 months, can be reduced to less than 8 months with a layer of centralized abstraction like CRIBL.
A direct impact on detection capacity
By filtering noise and optimizing formats, Data pipelines improve the relevance of the flows analyzed. Result: safety platforms gain speed, detection rate and reliability. SOC teams, for their part, access better structured data, more quickly questionable, and can focus on analysis rather than the maintenance of pipelines.
An adoption accelerated by UX and governance
The effectiveness of these platforms is also based on their fast handling and their transparent governance model. CRIBL, for example, offers a complete graphical interface, Git traceability of configurations, sandboxes for tests, and free access to 1 TB/day.
This concern for democratization contributes to their adoption: engineers can set up a proof-of-concept in a few days, without depending on the central IT. Some companies have thus replaced unstable kafka architectures of 40 TB/day by Cribl Stream, without a dedicated project team.
What this reveals of the future of Siem
The SIEM is no longer in the center. These are the data that is. And the ability to sort them, shape, drive and replay now conditions the performance, resilience and the scalability of cybersecurity architectures.
THE Data pipelines become the Strategic middleware Modern environments: both optimization brick, abstraction layer, and operational intelligence platform.
