Topic starter
18/08/2026 2:50 pm
TL;DR: AI SOC deployments underperform when unstructured logs, inconsistent field naming, and noisy telemetry force runtime preprocessing, eroding accuracy and speed, according to Axoflow. The practical shift is upstream normalization into open schemas and formats so automation reasons over predictable data instead of wrangling it.
Editorial analysis by NHI Mgmt Group, based on content published by Axoflow: “Why Your AI SOC Is Only as Good as the Data Feeding It”.
Key questions
Q: How should security teams prepare telemetry for AI-driven SOC automation?
A: They should normalise and enrich telemetry before it reaches the AI layer, using a consistent schema for the events that matter most.
Q: Why does schema inconsistency reduce AI detection quality?
A: Because the platform must spend runtime effort reconstructing meaning from logs that were never shaped for machine reasoning.
Q: What are the signs that an AI SOC data pipeline is failing?
A: Look for silent data gaps, drifting collector configurations, repeated field-mapping exceptions, and automation that becomes slower or less reliable as new sources are added.
Practitioner guidance
- Implement schema-driven normalisation at ingestion Convert vendor-specific telemetry into a common structure such as OCSF or OTel before it reaches AI-driven detection or response tools.
- Instrument the telemetry pipeline end to end Track source-to-destination flow health so silent gaps, drifting configurations, and dropped feeds are visible before they become blind spots.
- Filter low-value events before AI processing Remove redundant metadata and high-frequency noise early so downstream systems spend capacity on useful signals instead of repeated preprocessing.
Bottom line: AI SOC performance is limited by telemetry quality, not only by model capability, and schema drift is a primary source of that limitation.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
18/08/2026 3:32 pm
Schema readiness is now a governance requirement for AI security operations. AI SOC value depends less on model ambition than on whether the underlying telemetry is structured, complete, and stable enough for machine reasoning. Open schemas such as OCSF and OTel create the conditions for repeatable automation, while proprietary or inconsistent formats force each tool to rediscover meaning. Practitioners should treat schema readiness as part of the control plane, not as a data engineering afterthought.
A question worth separating out:
Q: What should organisations do when security telemetry sits in proprietary formats?
A: Move toward open schemas and open storage formats so the organisation, not the tool, controls how evidence is structured and queried. That preserves migration options, keeps historical data usable across platforms, and reduces the risk that a single vendor’s data model dictates future operating choices. Open data control is now a resilience issue, not just a cost issue.
👉 Read our full editorial: AI SOC performance depends on schema-ready security data