DVARA 1.1.0 is generally available. It makes three parts of a production deployment easier to use: native MCP protocol support, verified Keycloak and Auth0 SSO presets, and a Kubernetes deployment tested end to end on GKE and DOKS.
Everything below ships in every Enterprise install — the same governance core, the same MCP Proxy, the same agentic controls. 1.1.0 is a drop-in upgrade from 1.0.x.
DVARA 1.0.0 is generally available. It gives engineering and governance teams one place to set policy, protect sensitive data, control cost, and record what happened across LLM and MCP traffic.
This post covers why we built it, what is in 1.0.0-GA, and how to run it in your own infrastructure today.
An agent can keep sensitive data out of a model prompt and still leak it in the next tool call. MCP arguments and responses are separate data paths, so they need their own policy, PII handling, and audit controls.
An AI agent can call a tool, read the result, and decide what to do next. That makes it useful. It also means one bad decision can repeat at machine speed: another database query, another email, another refund attempt, another billable model call.