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19/08/2026 4:08 pm
TL;DR: Data quality, automated classification, and vendor-agnostic routing are now central to SecOps economics because downstream SIEM and AI tooling depend on clean pipelines, not just more ingest, according to Axoflow. The strategic shift is that pipeline governance is becoming the control point for reducing noise, cost, and operational friction.
NHIMG editorial — based on content published by Axoflow: The role of the pipeline at Splunk.conf25
By the numbers:
- Only 5.7% of organisations have full visibility into their service accounts.
- 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
- 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
Questions worth separating out
Q: How should security teams improve SIEM coverage without simply ingesting more data?
A: Start by mapping each data source to the detections it actually enables.
Q: Why does pipeline quality matter for identity and NHI monitoring?
A: Identity and NHI events often arrive as logs, tokens, API calls, and service-account activity that can be misparsed or dropped.
Q: What breaks when telemetry reduction is unmanaged in SecOps?
A: Unmanaged reduction can remove the very events analysts and detections depend on, especially when low-value and high-value logs are treated the same.
Practitioner guidance
- Define a telemetry quality baseline Measure parsing success, field completeness, duplicate rates, and routing loss before adding new data sources or detections.
- Treat log reduction as a policy decision Document which events are retained, downsampled, or discarded, and tie those decisions to detection use cases rather than storage convenience.
- Map identity signals through the pipeline Identify where service-account activity, API token events, and secrets exposure can be lost or distorted, then place validation controls at those points.
What's in the full article
Axoflow's full article covers the operational detail this post intentionally leaves for the source:
- How the automated processing engine handles classification, curation, reduction, and routing at scale
- The specific operational arguments behind vendor-agnostic normalisation across source systems and destinations
- Why the pipeline is positioned as the control point for SIEM economics and downstream AI use cases
- The conference context and practitioner conversations that shaped the article's thesis
👉 Read Axoflow's analysis of why the data pipeline is shaping SecOps and SIEM economics →
Data pipeline control in SecOps: what it means for SIEM teams?
Explore further
19/08/2026 4:41 pm
Pipeline ownership is now a security governance issue, not just an architecture preference. The article’s central thesis is that whoever controls classification and routing controls the economics and usefulness of detection. That makes the pipeline part of the security control stack, especially where noisy logs hide identity misuse or secrets exposure. Practitioners should treat pipeline ownership as a governance decision, not an infrastructure detail.
A question worth separating out:
Q: How do organisations know whether their security data pipeline is working?
A: They should track whether clean data reaches detections quickly and consistently, whether identity-related events remain searchable end to end, and whether AI or SIEM workflows make better decisions after pipeline changes. If these measures do not improve, the pipeline is still obscuring value.
👉 Read our full editorial: Data pipeline control is becoming the core issue in SecOps
