We build across AWS, Azure, and GCP. Different teams own each cloud and ship features independently, and our AI agents follow those same paths, calling services across multiple providers at once. Before Orca, that meant three different stories about where the agents lived and what they could reach. Orca connects to every account and subscription and gives us a single, contextual view of the risk tied to the AI agents and the services our business depends on. Our CISO can see the agents, their identities, and their blast radius across clouds in one place instead of chasing separate dashboards.

Our teams leaned hard into serverless. Developers, analysts, and even marketers writing Python scripts ended up shipping Lambda functions that touched important data. Traditional tools treated those functions as an afterthought. Orca sees serverless as part of the same unified risk surface, showing permissions, data access, and exposure in the context of the features we’re building. It lets our CISO enable that speed of creation instead of asking teams to slow down.

Our Kubernetes footprint grew faster than our security efforts could keep up with. Engineers and analysts were pushing workloads across clusters, and misconfigurations were getting lost in the complexity. Orca surfaces Kubernetes issues in the same unified context as everything else, and it helps show which misconfigurations are actually reachable from the internet or from the identities tied to our critical services. It also makes it clearer which Kubernetes misconfigurations sit on the paths that the AI agents, or the services they drive, could realistically reach. That added context is what lets a small team keep up with the clusters that power the features our business depends on.