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19/08/2026 3:54 pm
TL;DR: BigPanda says its observability footprint grew until manual fixes could not keep pace, then reports a 60% cost reduction and 90% ingest-volume reduction after shifting to Sawmills' AI-powered telemetry platform. The main governance lesson is that telemetry sprawl behaves like a control problem, not just a cost problem.
NHIMG editorial — based on content published by Sawmills: BigPanda cuts observability costs by 60% with AI
By the numbers:
- BigPanda reports a 60% reduction in observability costs after changing how telemetry is controlled and processed.
Questions worth separating out
Q: How should security teams govern AI-driven telemetry reduction?
A: Security teams should govern telemetry reduction through explicit policy, change control, and validation against detection requirements.
Q: Why do telemetry pipelines need identity and access controls?
A: Because collectors and exporters move sensitive operational data across environments, and the components that move that data must be trusted and bounded.
Q: What breaks when telemetry volume is managed only as a cost issue?
A: When telemetry is managed only as a cost issue, teams often over-filter data, lose important security events, and weaken incident response.
Practitioner guidance
- Centralise telemetry policy enforcement Define a single control layer for log sampling, suppression, enrichment, and retention so individual teams cannot expand ingest rules independently.
- Review privileged access to observability pipelines Inventory the service accounts, tokens, and admin roles that can modify telemetry flows, then reduce them to least privilege and require step-up approval for pipeline changes.
- Test filtering against incident-response needs Use known detection and forensic scenarios to verify that AI-based reduction does not remove security-relevant events, audit records, or chain-of-custody evidence.
What's in the full article
Sawmills' full article covers the operational detail this post intentionally leaves for the source:
- How the AI-powered telemetry platform reduced ingest volume across the observed environment
- The live webinar context with BigPanda's SRE and Sawmills' product leadership
- The reported workflow change from reactive firefighting to proactive telemetry control
- The specific before-and-after cost and volume outcomes described by the vendor
👉 Read Sawmills' analysis of AI-driven telemetry control and BigPanda's cost reduction →
AI telemetry control: what it means for cloud and SOC teams?
Explore further
19/08/2026 4:30 pm
Telemetry sprawl is now an identity-adjacent control surface. The article shows that observability cost is not just a FinOps issue. When workloads, service accounts, and pipelines can generate or reshape telemetry at scale, the control problem shifts to who can emit, enrich, suppress, and retain security data. Practitioners should treat telemetry rights as part of workload governance, not as an engineering afterthought.
A question worth separating out:
Q: How do teams know if telemetry optimisation is actually working?
A: It is working when cost falls without degrading alert fidelity, audit coverage, or forensic visibility. The key test is whether critical events still appear in the right tools, at the right time, with enough context to support investigation and accountability.
👉 Read our full editorial: AI-driven telemetry control cuts observability costs and ingest volume
