Topic starter
19/08/2026 3:52 pm
TL;DR: The traditional ingest-everything SIEM model is driving cost inflation, poor data quality, and operational burnout, according to Axoflow, while an autonomous data layer can cut telemetry volume by 50% or more and preserve better detection fidelity. The strategic shift is less about storage optimisation than about governing security data lifecycle, routing, and trust before analytics ever see the data.
Editorial analysis by NHI Mgmt Group, based on content published by Axoflow: “The Autonomous Data Layer: Control Your Data, Cost, and Cyber Risk”.
By the numbers:
- Organizations send about 50% more data than necessary into security pipelines, according to Axoflow.
- 62% of security teams report poor data quality, according to Axoflow.
Key questions
Q: What happens when organizations keep sending everything into the SIEM?
A: When organizations send everything into the SIEM, they pay to ingest noise, duplicate effort in manual cleanup, and still end up with weak detection data.
Q: Why does poor telemetry quality reduce both detection and compliance value?
A: Detection rules depend on consistent fields, and compliance evidence depends on reliable collection and delivery.
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.
Practitioner guidance
- Implement upstream telemetry classification Classify logs before they reach central analytics so high-value events are separated from noisy, low-value streams and handled differently.
- Define policy-based routing by data class Route security alerts, authentication logs and verbose diagnostics to different destinations based on intent, sensitivity and investigative value.
- Standardise on structured telemetry formats Normalize source data into consistent schemas so downstream detections, investigations and compliance workflows consume the same trusted records.
Bottom line: The core risk is not simply SIEM cost inflation but the governance failure that appears when organisations trust raw telemetry too late in the pipeline.
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19/08/2026 4:29 pm
Security data lakes expose a data governance problem, not just a storage problem: the central issue is that telemetry is being trusted too late, after cost has already been incurred and quality problems have already propagated. Central SIEM designs make sense only when data volume is low enough for manual remediation to keep pace. Once telemetry growth outstrips handling capacity, governance has to move upstream to routing, normalization and retention policy.
A question worth separating out:
Q: When should organisations prioritise data routing over SIEM storage expansion?
A: Prioritise routing when telemetry growth, licensing cost and analyst overload are rising faster than the team can compensate with manual tuning. At that point, adding storage only scales the problem. Policy-based routing creates leverage because it reduces volume before the expensive analytics layer sees it.
👉 Read our full editorial: Security data lakes are replacing SIEM ingestion sprawl