Multi-Cloud Detection Use Case
Every major cloud provider ships built-in threat detection — AWS has GuardDuty, Microsoft’s Azure has moved Sentinel under their Defender offering, and Google has Security Command Center. They work well enough inside their own walls. But they were built to protect their cloud, not yours-across-three.
When you try to stretch them across environments, you hit real limits. Sentinel can ingest AWS and GCP data, but those sources are second-class citizens — different schemas, separate parsers, and detection rules that were written for Azure first. GuardDuty and SCC don't even try. And Google SecOps, the strongest streaming architecture of the three, caps multi-event detection rules at 75.
Meanwhile, the attacks that actually matter don't respect cloud boundaries. A compromised identity in Azure AD that pivots to an AWS S3 bucket through a GCP-hosted application looks like three unrelated low-priority events — unless you can correlate them in real time across sources.
How spotr.io does it?
spotr.io treats every data source as first-class. AWS CloudTrail, Azure Activity Logs, GCP Audit Logs, on-prem syslog — all normalized on stream, enriched in flight, and evaluated against thousands of concurrent detections sub second. No 75-rule cap. No cross-cloud schema headaches. No trusting one cloud vendor's security tool with a competitor's data.
One engine. Any source. Real-time detection that doesn't degrade the further you get from a single vendor's ecosystem.