As our agents reason over regulated data—customer records and financial datasets—we needed confidence that they weren’t crossing residency or compliance boundaries. Orca shows where each agent runs, which data stores it can reach, and how those access paths align with our regulatory and policy constraints. That level of visibility lets us quickly spot an agent that could read out-of-region data or touch a store that should be locked down to specific roles.

We used to split our view of risk: AppSec tools looked at the code, cloud tools looked at the infrastructure, and nobody owned the full picture for the agents that sit on top of both. In reality, the marketer was writing the Python, the analyst was shipping the agents, and those agents call the APIs, touch the containers, and invoke serverless functions across clouds. Orca ties the application, cloud, and agent context together in one unified model. When there’s an issue, we can see the agent, the vulnerable code it relies on, the service it runs on, and the data it touches—all in a single view.

We are constantly creating with AI agents, and compliance used to feel completely disconnected from that reality. Teams ship agents to production, analysts wire agents into workflows, PMs push agent-backed features, and then audit season arrives with the spreadsheet. Orca continuously maps our cloud workload and agent posture to the frameworks we care about, tied directly to the resources and agents that power what we build.