The End of the Monolithic SIEM: Why Decoupled Security Architectures Are Growing In Popularity

The End of the Monolithic SIEM: Why Decoupled Security Architectures Are growing in popularity

Security experts are rethinking their data management strategy. The traditional security architecture, where every byte of telemetry is forwarded, indexed, and stored in a single monolithic SIEM is becoming operationally and financially unsustainable.

As cloud adoption and infrastructure complexity grow, data volumes explode. To stay within budget, engineering teams are forced to make binary choices: either pay exorbitant overage fees to maintain visibility, or indiscriminately drop logs to cut costs, creating blind spots. This approach treats data volume as the enemy, rather than an asset.

The root cause is structural. Monolithic SIEM architectures tightly couple storage and compute. You pay to index data that you may never query, and you pay for high-performance retrieval on data that holds low security value. Modern security operations require a fundamental architectural shift: moving the control plane out of the analytics layer and into a dedicated security data pipeline, according to Software Analyst Cyber Research.

‍

Why Monolithic SIEM Architectures Fail: Noise, Schema Drift, and Cost?

Before discussing solutions, it is critical to understand what breaks in the current stack.

1. Ingestion-Based Pricing Traps

Legacy pricing models reward data volume, not data quality. When a firewall sends verbose, repetitive logs, the SIEM charges for every gigabyte ingested, regardless of investigative value. Teams often spend more budget storing "noise" (debug logs, duplicates, and irrelevant metadata) than on high-priority alerts. Cost reduction becomes a manual, reactive process of suppressing logs after the bill arrives. This financial pressure is driving the shift toward pipelines that can filter unwanted data at the source. A sudden spike in DNS logs doubles ingestion overnight. Nothing is wrong operationally, but the ingestion bill doubles. The SOC disables logging to stay within budget. The failure isn’t volume; it’s that the decision about data value happens after ingestion, when it’s already expensive.

2. Downstream Analytical Failure

When raw, unstructured data is dumped into a SIEM, detection logic fails. Analysts must write complex queries to normalize fields across disparate vendors (e.g., mapping src_ip from one firewall and SourceAddress from another). This "schema-on-read" approach adds latency to investigations. Furthermore, if a vendor updates their log format (schema drift), detection rules break silently, leaving the SOC exposed until an engineer manually fixes the parser. Pipelines are now expected to detect schema drift and auto-generate parsers to prevent these failures.

3. AI Degradation

The industry is pushing for AI-driven SOCs, but AI models don’t work well when fed unstructured or inconsistent telemetry. Without a layer to normalize and enrich data before it reaches the model, AI tools are prone to hallucinations and inability to correlate events across different data sources. Pipelines function as the preparation layer for AI, ensuring data is clean and structured.

A vendor changes a log field during a firmware update. Nothing fails visibly. Alerts simply stop triggering because the detection logic no longer matches. The problem isn’t the detection rule; it’s that normalization happens downstream, after the data has already changed shape.

What Is a Decoupled SIEM Architecture?

A decoupled SIEM architecture separates data collection, processing, storage, and analytics into independent layers. Instead of sending all telemetry directly into a SIEM for indexing, data is first processed in a security data pipeline where it can be normalized, filtered, enriched, and routed based on policy. The SIEM becomes an analytics layer rather than the system responsible for data preparation.

The Shift to Decoupled Security Architecture

To solve these failure modes, security architecture is moving toward a decoupled model. In this design, the responsibility for data collection, processing, and storage is separated from the analytics and detection layer.

The shift is happening now because cloud-native infrastructure, ingestion-based pricing, and AI-driven analysis all increase the cost of unstructured telemetry. The security data pipeline becomes the new control plane. Instead of a passive conduit, the pipeline acts as an intelligent router and refinery. It governs ingestion, normalization, and routing, ensuring that downstream platforms receive only clean, structured, and relevant data.

Intelligent Routing and Tiered Storage

Decoupled architectures allow teams to align storage costs with data value. High-value security events (e.g., EDR alerts, identity logs) are routed to the "hot" tier in the SIEM for real-time detection. High-volume, low-value logs (e.g., VPC flow logs, compliance archives) are routed to cost-effective object storage (S3-compatible "cold" tiers).

This structure enables "selective rehydration." Data remains in cheap storage until it is needed for an investigation, at which point it can be retrieved and replayed into the analytics tool. This capability prevents data loss during outages and allows for retrospective hunting without the cost of keeping all data indexed forever.

In a monolithic architecture, the SIEM decides what data is stored, structured, and searchable because it owns ingestion. In a decoupled architecture, that responsibility moves upstream. The pipeline prepares and routes data, the SIEM analyzes it, and the operator defines policy.

Axoflow: The Security Data Layer

Axoflow functions as this security data layer, providing the mechanism to decouple telemetry from destination constraints. It sits between the infrastructure and the analytics platforms, giving operators granular control over data flow.

Normalization and Schema Discipline

Axoflow addresses the data quality issue by normalizing logs in transit. Using capabilities like the Open Cybersecurity Schema Framework (OCSF), Axoflow structures raw logs into a consistent format before they reach the SIEM. This "structure-at-ingest" approach ensures that downstream detection rules remain stable even if the source format changes.

By handling classification and parsing at the pipeline layer, Axoflow offloads the heavy lifting from the SIEM. For example, converting firewall logs into structured metrics or UDM format slashes false positives and standardizes the data for immediate use by AI tools or analysts.

Noise Reduction and Payload Trimming

Axoflow provides operators with the tools to perform context-aware suppression and payload trimming. This involves identifying specific fields or recurring events that provide no security context and removing them from the stream.

For deployments involving Splunk, Axoflow can classify and enrich data with the proper sourcetype and route it to the correct index via the HTTP Event Collector (HEC). This reduces the licensing burden by filtering out redundant data while ensuring the events that do land are enriched and query-ready.

Integration Health and "Silence Detection"

A critical, often overlooked risk is "silence" - when a log source disconnects or stops sending data. In a coupled architecture, this might go unnoticed until an incident occurs. Axoflow treats telemetry as infrastructure, providing health monitoring to detect silent failures, schema drift, and volume anomalies. This ensures the SOC is aware of blind spots immediately.

Architectural Takeaway

The era of the "ingest everything" monolithic SIEM is over. It is operationally brittle and financially inefficient.

Security architects must transition to a decoupled architecture where the security data pipeline serves as the control plane. By centralizing normalization, enrichment, and routing within the pipeline layer, organizations regain ownership of their data. This approach stabilizes costs, improves detection fidelity, and ensures that the SOC runs on high-quality telemetry rather than raw noise.

Frequently Asked Questions About Decoupled SIEM Architecture

Why are monolithic SIEMs becoming expensive?

Because ingestion-based pricing couples storage and compute, forcing organizations to pay to index data regardless of investigative value. Plus ofcourse there is a 25% YoY data growth…

What does a security data pipeline do?

A security data pipeline prepares telemetry before analytics by normalizing formats, enriching context, and routing data based on value and retention policy.