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Observo’s AI-native data pipelines cut noisy telemetry by 70%, strengthening enterprise security
The reduction in noisy, unstructured telemetry data by Observo can cut enterprise observability costs by up to 50%.
AI models need massive datasets to train on, and the workloads they power — whether internal tools or customer-facing apps — are generating a flood of telemetry data: logs, metrics, traces and more. It’s true that some security information and event management (SIEM) systems and observability tools have rule-based filters to cut down the noise, but that rigid approach doesn’t evolve in response to surging data volumes. In one case, a large North American hospital was struggling with the growing volume of security telemetry from different sources, leading to thousands of insignificant alerts and massive expenses for Azure Sentinel SIEM, data retention and compute.
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