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19/08/2026 3:30 pm
TL;DR: Monolithic SIEM architectures are becoming operationally brittle because ingestion-based pricing, schema drift, and unstructured telemetry push teams to choose between visibility and cost, according to Axoflow. The shift to a decoupled security data pipeline moves normalization, routing, and storage decisions upstream, which changes how SOCs control telemetry quality and analytics readiness.
NHIMG editorial — based on content published by Axoflow: The End of the Monolithic SIEM: Why Decoupled Security Architectures Are Growing In Popularity
By the numbers:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job.
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
- Systems with least-privileged AI access had a 17% incident rate versus 76% for over-privileged systems.
Questions worth separating out
Q: How should security teams implement decoupled telemetry architectures without losing visibility?
A: Start by separating collection, normalization, routing, and retention from analytics.
Q: Why do monolithic SIEMs create cost and detection problems at the same time?
A: They charge for ingestion before teams know whether the data is useful, so organisations either pay to index noise or suppress logs and create blind spots.
Q: What breaks when schema normalization happens only inside the SIEM?
A: Correlation rules become brittle, field mappings drift across sources, and analyst work increases as each log format demands exception handling.
Practitioner guidance
- Map telemetry by security value before ingestion Classify sources into hot, warm, and cold paths based on investigative value, then route high-signal identity, EDR, and workload events to analytics while archiving low-value noise elsewhere.
- Normalize logs in transit Move parsing, enrichment, and schema translation upstream so logs arrive in a stable format such as OCSF before detection rules or AI workflows consume them.
- Add silence detection to pipeline monitoring Alert when expected sources stop reporting, because a missing log stream can hide credential abuse, account misuse, or non-human identity activity.
What's in the full article
Axoflow's full analysis covers the operational detail this post intentionally leaves for the source:
- Concrete examples of how a security data pipeline normalises logs before they reach SIEM and AI tools
- Detailed routing patterns for hot, warm, and cold telemetry storage across different event types
- Examples of schema drift and parser failure handling in real SOC environments
- Operational treatment of silence detection and source-health monitoring in decoupled architectures
👉 Read Axoflow's analysis of decoupled SIEM architecture and security data pipelines →
Decoupled SIEM architectures: what they mean for SOC teams?
Explore further
19/08/2026 4:13 pm
Decoupled telemetry is becoming a governance problem, not just a storage problem. Once logging, indexing, and retention are separated, teams must decide which signals deserve real-time analytics, which belong in cold storage, and which should never be ingested at full fidelity. That is a control-plane decision, not a tooling preference. For IAM and NHI programmes, the key question is whether identity and service-account telemetry is being preserved at the level needed for investigation and audit.
A question worth separating out:
Q: How do you know if a security data pipeline is actually improving operations?
A: Look for fewer parser-driven alert failures, lower ingest waste, faster investigations, and visible detection of telemetry silence. A healthy pipeline should preserve evidence quality while reducing the amount of raw noise the SIEM has to index. If visibility improves only by adding cost, the model is still broken.
👉 Read our full editorial: Decoupled SIEM architectures are reshaping SOC data control
