The data scientist on our team deployed an agent backed by a model endpoint connected directly to production data. Orca immediately flagged the exposed inference endpoint, mapped the agent’s access, and gave us the context we needed to lock it down. The agent and the model stayed live; the risk didn’t. For an AI-heavy org, having a platform that understands agents, their model endpoints, and the data they touch as part of the attack surface was exactly what we needed.

Our agents can chain actions across systems—invoking tools, writing to storage, and updating records—and that kind of power carries real risk if the workflows aren’t properly governed. Orcas context makes it clear that agents may touch sensitive data, change configurations, or even reach the public edge, so we can see exactly where strong guardrails and human-in-the-loop checks need to sit. Rather than discovering risky agent behavior only after something breaks, we’re able to put policy in place where it matters most.

Our first wave of AI agents carried broad, long-lived permissions “just in case,” which quietly inflated the blast radius of each workflow. Orca shows effective permissions and actual usage side by side for agents and identities, so we can clearly see which rights are actually exercised and which are dead weight. That context gave us the confidence to move toward just-in-time access.