MiRuntime.com
Governance and Trust
A governance model for Machine Intelligence Runtime based on permissions, local control, evidence trails, approvals, and explicit runtime boundaries.
Trust in AI systems will not come from better slogans. It will come from runtime behavior that users can inspect, constrain, approve, and correct.
Trust is an operational property
A Machine Intelligence Runtime should make trust observable. Users should know when a model is guessing, when a tool is about to run, when private context is being used, and when a decision requires human review.
Permission transparency
Show what the runtime can access and what each tool is allowed to do. Separate read access from write access.
Human review
Require approval for sensitive, irreversible, externally visible, or high-impact actions. The runtime should not normalize silent automation.
Evidence-first output
Support conclusions with traceable sources, file references, tool outputs, and run metadata where the task requires it.
Memory control
Let users inspect, correct, scope, export, and delete durable memory. Hidden memory creates hidden governance risk.
Governance controls
- Capability registry for models, tools, connectors, and local endpoints.
- Policy rules for data classes, user roles, and action categories.
- Approval queues for high-impact workflows.
- Evidence ledger for tool calls, artifacts, and changed state.
- Run cancellation and checkpoint recovery.
- Memory lifecycle controls and project scoping.
Runtime test: if the system cannot show what it did, it is not ready to act on the user’s behalf.