Case Study: Large North American Hospital cuts Azure Sentinel costs by over 50% with Observo AI

Hospital System Cuts Azure Sentinel Costs by Over 50% with Observo.ai

Large North American Hospital was struggling with rapidly growing Microsoft Azure Sentinel costs and lagging query performance as volumes of telemetry from Cisco ASA firewall logs, Windows Event Logs, Active Directory, domain controller and DNS logs surged. The SOC was overwhelmed by false positives and ad‑hoc pipelines that forced manual sampling and occasional data shutoffs, threatening visibility and compliance. The hospital engaged Observo AI and its AI‑powered observability pipeline to find a long‑term, automated approach to data optimization and sensitive‑data discovery.

Observo AI implemented data‑source specific reduction, dynamic enrichment, a searchable low‑cost security data lake, and on‑demand rehydration of full‑fidelity logs. The deployment cut initial Sentinel ingestion by more than 78% (projected to exceed 85% in three months), reduced total Sentinel costs by over 50%, and improved incident handling with a 35% reduction in mean time to resolve critical incidents, while preserving all data in a low‑cost lake for compliance and later analysis.