A sandbox environment is a space where code or a model runs sealed off from the live system. Whatever happens inside does not leak out. No real customer data, no real payments, no real email leaving the building. The aim is not to stop things breaking, it is to make sure nobody is affected when they do.
Isolation gets built in layers. A separate database with synthetic data, outbound calls redirected to test endpoints, restricted file system and network access, and resource limits are the usual pieces. Cloud providers typically deliver this at the container or lightweight virtual machine level.
Sandboxes carry more weight in AI work than they used to. Once a model can write and run code, or an agent can call tools on its own, nobody knows in advance what the generated command will do. So code execution, file operations and outbound API calls happen inside an isolated environment, and results only leave it after a check.
A concrete case: an analysis assistant writes Python to read a table the user uploaded and draw a chart. That code runs in a sandbox. Even if it tries to delete files or reach the open internet by mistake, it hits the boundary of the environment, and everything is wiped when the session ends.
A sandbox is not sufficient on its own. Escapes are possible, so permission checks and audit logging sit on top of it.
