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02/08/2026 11:58 am
TL;DR: Security teams are increasingly treating telemetry as infrastructure, not exhaust, because siloed data, inconsistent governance, and manual handling slow detection and undermine AI readiness, according to DataBahn. The governance problem is structural: without portable, observable, and well-contextualised security data, modern SOCs cannot trust automation or scale operations effectively.
NHIMG editorial — based on content published by DataBahn: Why are Legacy SIEMs a problem?
By the numbers:
- This week, we shared that Databahn has grown by more than 400% year-on-year.
- A Fortune 100 global airline managed a complex SIEM migration in just 6 weeks.
- Sunrun reduced log volume by 70% while improving visibility across its complex and geographically distributed environment.
Questions worth separating out
Q: How should security teams govern security data across multiple tools and pipelines?
A: Security teams should define data ownership, lineage, and transformation rules before expanding the tool stack.
Q: Why does fragmented telemetry create risk for AI-enabled SOC operations?
A: Fragmented telemetry weakens AI because models inherit the quality and consistency of their inputs.
Q: What breaks when security data is not portable across environments?
A: Without portability, teams lose context during migrations, re-platforming, and cross-tool investigations.
Practitioner guidance
- Map security data lineage end to end Document where telemetry originates, how it is transformed, where context is added, and which teams own each stage.
- Move enrichment before ingestion where possible Attach threat, identity, and asset context before events reach the SIEM so routing and retention decisions reflect value, not just volume.
- Set governance rules for telemetry portability Define which fields must survive tool changes, migration, and re-platforming without manual reconstruction.
What's in the full article
DataBahn's full article covers the operational detail this post intentionally leaves for the source:
- The specific enterprise migration and data-governance scenarios behind the growth story, including how teams handled large-scale log movement.
- Practical examples of how upstream enrichment and routing were applied to reduce volume without losing investigative value.
- The article's discussion of AI readiness and why data foundations had to be stabilised before adding more intelligence.
- The market-positioning detail around staying focused on security data management rather than broadening into adjacent platform categories.
👉 Read DataBahn's analysis of why security data has become strategic architecture →
Security data governance in modern SOCs: what teams are missing?
Explore further
02/08/2026 1:05 pm
Security data governance is now part of security architecture, not a support function. When data lineage is unclear, every downstream security decision becomes less reliable, from alerting to automation. That makes the data layer a governance domain in its own right, especially where identity, access, and NHI telemetry must be correlated across systems. Practitioners should treat data governability as a prerequisite for operational security.
A question worth separating out:
Q: How do organisations know whether their security data foundation is working?
A: Look for fewer manual fixes, faster migrations, cleaner routing decisions, and less analyst time spent correcting schemas or chasing missing context. A working foundation makes telemetry easier to trust and easier to reuse. If every new initiative depends on engineering intervention, the data layer is still fragile.
👉 Read our full editorial: Security data governance is now the foundation of modern SOCs
