This private research university operates a highly distributed security environment spanning multiple campuses, thousands of users, and a wide range of academic and research systems. Its security operations rely heavily on continuous telemetry flowing into a SIEM for detection, investigation, and compliance.

As ingestion volumes and operational complexity increased, the university initiated a transition to a new SIEM platform. The migration required both the existing and new SIEMs to run in parallel, while continuing to send telemetry to a security data lake. Maintaining trust, visibility, and detection coverage during this transition became critical.

“At the outset, the goal was to help the security team regain control over data volumes so they could focus on a complex SIEM migration without increasing operational risk.”

— SI Project Leader / Engagement Manager

As the migration effort began, the security team faced several compounding challenges that made progress risky and expensive.

• Rising ingest volume and cost pressure
  High-volume firewall and application telemetry continuously streamed into the SIEM, driving up ingest costs and limiting flexibility to optimize without risk.
• Parallel SIEM operations during migration
  The university needed to route telemetry simultaneously to the existing SIEM, the new SIEM, and a security data lake — without duplicate ingestion or fragmented pipelines.
• Limited visibility into data quality and health
  Schema drift, malformed fields, incorrect timestamps, and silent sources reduced analyst confidence and made troubleshooting difficult.
• Lack of real-time insight into data behavior
  Unexpected volume spikes, drops, or source failures often went unnoticed until they impacted investigations or escalated operationally.

Any attempt to reduce volume without clear visibility created concern that detections, alerts, or investigative workflows could be compromised.

As part of the SIEM transition, the university set out to establish a stable and transparent foundation for managing security telemetry across multiple destinations. The goal was to reduce cost and operational overhead while maintaining full trust in security outcomes.

They needed a solution that could:

• Reduce SIEM ingestion volume and cost without impacting detection coverage
• Support parallel routing to the existing SIEM, new SIEM, and a security data lake
• Provide consistent visibility into data quality, telemetry health, and logging posture
• Apply enrichment and correlation to improve asset attribution and investigations
• Reduce operational complexity and avoid vendor lock-in during and after migration

Databahn was introduced as the centralized control layer for the university’s security telemetry, working alongside a Big 4 systems integrator supporting the broader SIEM migration. The platform provided a single place to manage collection, reduction, validation, and routing—without disrupting existing data flows.

• Centralized telemetry control fabric
  Databahn established a unified layer to observe, manage, and govern telemetry flowing across sources and destinations.
• Rule-based volume optimization
  Intelligent volume control rules were applied to high-volume firewall and security telemetry, enabling safe reduction while preserving detection fidelity.
• Multi-destination routing
  Telemetry was routed in parallel to the existing SIEM, the new SIEM, and the security data lake, maintaining full-fidelity data outside the SIEM while forwarding only relevant events.
• Data quality validation and normalization
  Malformed fields, schema drift, timestamp inconsistencies, and silent sources were detected and addressed before data reached downstream systems.
• Telemetry posture and behavioral insight
  Real-time visibility into data flow behavior enabled teams to identify anomalies, source disruptions, and pipeline issues early.

“Databahn demonstrated that volume optimization and strong security posture are not mutually exclusive. That gave the team the confidence to move forward far more efficiently than expected.”

— SI Project Leader

With centralized control and visibility in place, the university was able to move forward with confidence and significantly accelerate its SIEM migration.

• ~60% reduction in SIEM ingest volume
  Volume optimization was applied upstream, reducing cost without affecting detection or investigations.
• Faster, lower-risk SIEM migration
  Databahn met the project’s success criteria in just two days, while competing solutions had not done so after six weeks.
• Improved trust in data quality
  Analysts gained consistent, reliable data with clear visibility into health, coverage, and transformation.
• Operational alerting and anomaly detection
  The team could proactively identify abnormal volume changes, source failures, and pipeline disruptions.
• Reduced operational overhead
  New sources were onboarded smoothly without rebuilding pipelines or custom logic, supporting long-term flexibility.
  
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