A higher education university grappled with a sprawling security monitoring setup across multiple platforms, inhibiting a unified view of security events and impeding timely responses to cyber threats. Spyglass intervened, creating a centralized analytics data model housed in an Azure Data Lakehouse, consolidating diverse monitoring data into a cost-effective, efficient cloud solution. Ingestion pipelines were established to load the warehouse, enabling ML and analytics tools to access the data.
This transformation empowered the client to correlate vast amounts of disparate data, unveiling previously hidden security patterns. Moreover, the ability to build ML models for predictive analysis now arms them to anticipate and prevent future attacks proactively.