Our product team constantly runs experiments, turning features on and off with flags that are increasingly evaluated or executed by AI agents. Orca doesn’t try to live in the feature-flag layer; instead, it watches the underlying cloud identity and the agent posture. When an experiment gives an agent broader access than intended, or opens up a test path to sensitive data, Orca picks it up on its next assessment of the cloud and agent surface and flags it. Developers don’t have to change how they ship experiments, but security can still see where agent-driven flags might create unexpected exposure.

Our CISO used to get disparate stories about the AI agents: AppSec had one view, cloud security had another, and no one owned a single picture of how agent identities and the data all fit together. Orca’s top-level posture view now gives leadership one pane where every AI agent shows up alongside traditional services, with clear exposure, effective permissions, and blast radius. When the CISO walks into a meeting, they can see which agents carry meaningful risk, what data is at stake, and how that ties back to what the business is building next.

Our teams are drowning in alerts, and the dashboards barely mention AI agents, even though those agents are quietly involved in many of the riskiest paths. Orca changes that by presenting a prioritized view where AI agents, their permissions, and the data they can touch show up alongside traditional services. As a result, we spend less time flipping between tools and more time focused on the short list of agent-driven attack paths that actually matter to the business.