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Observability

Taking Control of Log Management Costs with Smarter Observability Pipelines

March 9, 2026  ·  5 min read  ·  HIT Services

This guide is vendor-neutral. It synthesises public information on log-data growth and observability-pipeline strategies, citing CISA SIEM ingestion guidance and NIST log-management frameworks.

Why Log Management Costs Are Surging

Enterprises are generating exponentially more telemetry — logs, metrics, traces — as cloud adoption and microservices architectures expand. Public estimates show 35% year-over-year growth in log volume, with some cloud log categories (VPC Flow Logs, Kubernetes logs, CloudTrail) growing up to 5× annually.

This growth overwhelms traditional log platforms, increasing storage costs, reducing analyst productivity, and creating operational complexity. Independent reporting highlights that up to 70% of machine-generated data provides little security or observability value.

Why Observability Pipelines Matter

Observability pipelines — also called telemetry pipelines — sit between data sources and destinations (SIEMs, data lakes, monitoring tools). They parse, filter, enrich, and route data to the correct location. Their purpose is to regain control over volume, cost, and quality. CISA and NIST support this architectural separation, emphasising preprocessing, prioritisation, and lifecycle-appropriate routing.

Five Vendor-Neutral Strategies to Regain Control

1. Filter Routine, Low-Value Events

Routine sign-outs, health checks, and repetitive metadata increase cost but add little detection value. Filtering before ingestion reduces SIEM and observability spend significantly.

2. Reduce Redundancy & Noise

Noisy telemetry — duplicate records, verbose debug logs — slows analysts and increases false positives. Public analysis shows noisy data can represent up to 70% of enterprise telemetry.

3. Route Logs Based on Purpose

CISA recommends prioritising logs by security value: critical identity, endpoint, and network-security events go to SIEM; high-volume operational logs go to cheaper storage tiers.

4. Schema Normalisation & Field Pruning

NIST highlights the cost impact of inconsistent log formats. Standardising schemas and removing unused fields lowers storage footprints and improves query performance.

5. Tiered Retention Policies

Keep hot data for days, warm data for weeks, and move compliance-required logs to cold archival storage. Aligned with federal retention and logging recommendations for cost efficiency.

The Impact: Lower Cost, Higher Signal Quality

Optimised observability pipelines can reduce telemetry volume by more than half while improving incident-response visibility by over 40%. By pushing only high-value data to expensive tooling and routing everything else appropriately, organisations regain control and reduce operational overhead.

30-Day Implementation Plan

  1. Identify high-volume sources: baseline GB/day and cost contribution. Most organisations find 3–5 sources driving the majority of spend.
  2. Classify logs by security & observability value: align with CISA SIEM ingestion priorities.
  3. Deploy pipeline preprocessing: filtering, deduplication, enrichment — before logs hit SIEM or observability tools.
  4. Shift archival logs to cold storage: maintain compliance without SIEM inflation.
  5. Track KPIs weekly: volume reduction, cost per TB/day, alert fidelity impact, analyst workload change.

FAQs

Does filtering reduce incident visibility?

No — industry best practices recommend filtering only routine, low-risk events while archiving full-fidelity logs in cold, inexpensive storage for forensics.

How do we avoid losing critical signals?

Use ATT&CK-aligned detection mapping and prioritise logs per CISA guidance. Signals needed for real-time detection remain in hot storage; everything else is preserved in cheaper tiers.

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