There was a recurring problem with shadow data: production data getting copied into staging and dev accounts that were nowhere near as locked down, plus data stores nobody even remembered creating. Orca discovers sensitive data across managed, unmanaged, and shadow stores, including files on VMs, containers, and buckets, and then classifies it against categories like PII and payment data. Seeing a forgotten production snapshot sitting in a loosely controlled dev account, and having that tied to how exposed the account was, makes it obvious which copies are genuinely risky versus intentional.

A lot of attention goes to containers these days, but there are still plenty of VMs and long-lived hosts, and those were exactly where our old scanning was the patchiest. Orca covers them in the same model as everything else, showing which host vulnerabilities sit on internet-facing machines, which are tied to privileged identities, and which are genuinely reachable versus buried behind existing controls. That added context lets us prioritize host patching based on real exposure instead of relying on raw CVSS scores.

Our business moves fast. Developers, analysts, and PMs are constantly shipping, and AI agents are woven into many of those workflows. We needed visibility that could keep up with that pace—not a slow rollout that would still be incomplete by the time the next agent went live. Orcca connected to our cloud account, and within days it gave us a real risk picture, including the AI agents: the identities they run as, the endpoints they expose, and the data they could touch. We didn’t have to chase teams to get a clear understanding of our agent services.