Evaluate AI risk without sending sensitive data outside your boundary.
DriftGard local evaluation modes let teams enforce policy against sensitive prompts, responses, conversations, and approved model usage while keeping regulated content inside their own environment.
Built for zero-egress workflows
Send verdict metadata to dashboards while content stays local.
Local checks. Central governance.
Sensitive content can stay inside your environment while DriftGard still creates a consistent policy, verdict, model governance, and evidence workflow.
Local policy execution
Run policy evaluation close to the application so prompts, responses, and tool arguments do not need to leave your trust boundary.
Runtime config sync
SDKs fetch the active control pack and lightweight project runtime config, including approved model controls, during initialization and refresh.
Metadata reporting
Optionally report verdicts, risk scores, policy IDs, model governance violations, and categories without exposing regulated content.
Consistent evidence
Maintain the same compliance workflow across hosted, private, and local deployment models.
Designed for sensitive AI systems.
Need zero-egress governance?
Run a pilot against one sensitive AI workflow and validate local evaluation end to end.
Request Local Evaluation Demo