Routing
Directs each event to the appropriate destination based on content and value. High-fidelity detection data to SIEM, compliance data to cheaper storage.
Security Operations
Fix Your SIEM's Cost and Noise Problem with a Security Data Pipeline
Most organizations with SIEM platforms face the same reality: expensive licensing driven by data volume and critical signals buried in noise. A security data pipeline addresses the root cause by controlling what goes into your SIEM—routing the right data to the right destination in the right format at the right cost.
Reduce SIEM ingest costs by 20-40% while improving detection fidelity through intelligent data processing.
Purpose-built security data pipeline implementations using Cribl and other leading platforms.
A Different Approach
Control what goes into your SIEM before it becomes a cost and noise problem
Enterprise environments generate telemetry faster than SIEM economics can handle. Every endpoint, network device, cloud platform, and security tool generates events. The SIEM licensing model creates a direct financial incentive to ingest less data, but security teams are reluctant to decide in advance which logs they won't need for future investigations.
The Data Quality and Cost Problem
SIEM platforms create an operational reality where costs are driven by volume, not value, while critical signals get buried in noise.
SIEM licensing costs driven by high-volume, low-value sources
Analyst fatigue from false positives generated by noisy data
Silent detection gaps when data sources stop logging
Inconsistent field naming that breaks detection content
Forensic investigations delayed by unstructured data
Security Data Pipeline Functions
A security data pipeline operates across five core functions that address both cost and signal quality.
Filtering
Removes events that provide no security value before they reach the SIEM. Drops high-volume benign events and duplicate logs.
Normalization
Standardizes field names, timestamps, and event classifications across all sources for consistent detection content.
Enrichment
Adds context at ingestion: geolocation, threat intelligence, asset classification. Runs once instead of at every query.
Replay
Reprocesses historical data from cheaper storage through new detection logic without continuous SIEM indexing costs.
Implementation Deliverables
What you get from a security data pipeline implementation.
Pipeline Architecture
Designed routing, filtering, and normalization rules based on your specific data sources and SIEM platform.
Cost Optimization
Identification and filtering of high-volume, low-value sources with measurable SIEM cost reduction.
Detection Enhancement
Normalized schemas and enriched events that improve detection fidelity and reduce investigation time.
Operational Impact
- 20-40% reduction in SIEM ingest volume and licensing costs
- Improved detection fidelity from normalized, enriched data
- Forensic capability preserved through tiered storage
- Reduced analyst investigation time with immediate context
Best Fit Organizations
- Significant Splunk or volume-licensed SIEM deployments
- Security teams experiencing alert fatigue from noisy data
- Environments adding new cloud platforms requiring normalization
- Compliance programs requiring long-term log retention
Platform Options
Security data pipeline platforms for different environments
Choose the right platform based on your existing SIEM, data volume, and operational requirements.
Recommended
Cribl Stream
Leading Platform
Vendor-neutral platform with extensive library of security source configurations and real-time processing.
Best Fit
Organizations with diverse security tool environments requiring flexible routing and normalization.
Native SIEM Features
Built-in data processing capabilities within existing SIEM platforms like Splunk or Sentinel.
Best Fit
Single-vendor environments with simpler data processing requirements.
Custom Pipeline
Purpose-built data processing using open-source tools and custom development.
Best Fit
Organizations with specific requirements and internal development resources.
Why IVI
Security operations expertise with data pipeline specialization
SIEM Cost Optimization Experience
Deep experience with Splunk, Sentinel, and other volume-licensed SIEM platforms.
Proven Results
Consistent 20-40% SIEM cost reductions through intelligent data pipeline implementation.
Security-First Data Architecture
Pipeline designs that preserve forensic capability while optimizing for detection fidelity.
Balanced Approach
Cost reduction without compromising security investigation capabilities or compliance requirements.
Related Resources
Related Resources
Review related solution pages, supporting materials, and additional resources that help explain where this solution fits and how it can be applied.
FAQs
Frequently Asked Questions
Common questions about security data pipeline implementation.
Does a security data pipeline replace our SIEM?
No. The pipeline sits upstream of your SIEM and improves what reaches it. Splunk, Microsoft Sentinel, and other SIEM platforms remain the detection and investigation layer. The pipeline makes them work better and cost less to operate.
What is Cribl and how does it fit into this approach?
Cribl is a leading security data pipeline platform that processes events in real time, supports an extensive library of purpose-built configurations for common security sources, and routes data to any destination. It is vendor-neutral on both source and destination sides.
How quickly can a pipeline reduce our SIEM costs?
For environments with identifiable high-volume, low-value sources—which most have—meaningful cost reduction can be achievable within the first 60-90 days of deployment. The full benefit is realized as the pipeline is extended to additional sources over time.
Will filtering data create blind spots in our security monitoring?
Properly implemented filtering removes only events with no security value while preserving all detection-relevant data. The pipeline can also route filtered data to cheaper storage for compliance retention, maintaining forensic capability without SIEM indexing costs.
How does data normalization improve detection effectiveness?
Normalization standardizes field names, timestamp formats, and event classifications across all sources. This means detection content built against a standard schema works reliably across every source, not just the ones that happen to match the expected format.
What happens to our existing SIEM detection content?
Existing detection rules continue to work, often with improved reliability due to normalized data schemas. The pipeline can be configured to maintain backward compatibility while gradually improving data quality and consistency.