A server goes dark at 3:14 a.m. Logs scatter across three clouds. A question burns: Who accessed what and when?
Multi-cloud environments make that question harder than it should be. Identities span AWS, Azure, and GCP. Each platform logs access in its own format. Timestamps drift. Event types differ. Linking one user’s actions across clouds demands precision. Every delay, every missing log, is an open door for risk.
The core problem: fragmented access visibility. In multi-cloud, you may have hundreds of services, accounts, and endpoints. Authentication pathways vary—IAM roles in one, service principals in another, API keys in a third. Without a unified view, anomaly detection weakens. Audit trails lose meaning. Compliance teams dig through raw exports, trying to assemble a timeline from scattered evidence.
Solving who accessed what and when across multi-cloud means centralizing events fast. Merge log streams in near real-time. Normalize fields—user IDs, request sources, resource paths, timestamps—into one schema. Tag every record with cloud origin. Store them in a searchable datastore. This allows you to query, “Show me every access to sensitive bucket X across all clouds in the last 24 hours,” and get an answer instantly.