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.

We used to drown in container CVE lists that didn’t reflect how our teams—or the AI agents—actually use those services. Developers were shipping images quickly, product managers were prototyping and pushing features, and the agents were calling into containers in ways that security couldn’t easily prioritize. Orca shows which container vulnerabilities sit on the assets that are truly exposed, and more importantly, which ones fall along the paths our AI agents and their tools actually traverse. It filters out the noise and surfaces the container risks that could affect real agent workflows, not just theoretical package issues.