Runtime Governance
Governance principles, public charters, and operational boundaries for governed AI systems.
Disclosure Boundary
Public materials describe research direction, governance principles, operational abstractions, and public-safe artifacts. Internal runtime topology, implementation details, security-sensitive workflows, and restricted system infrastructure are withheld pending review.
Published materials are classified as PUBLIC, PUBLIC-SAFE, or PENDING REVIEW. Internal, restricted, and lab-only classifications are not represented on this surface.
Governance Thesis
DBRL studies governance as a runtime property, not only a policy document. A governed AI system should expose its authority boundaries, preserve human decision rights, record evidence, and block irreversible actions unless explicit conditions are satisfied.
Runtime Governance Principles
Autonomy expands only with evidence. High-impact actions require staged review, rollback paths, and operator-visible state. The model is a component; the runtime enforces admissibility.
Irreversible Action Thresholds
Not every model output warrants the same authority. DBRL research distinguishes low-risk cognitive work from actions that mutate external state, spend resources, or commit irreversible decisions — each class requires stronger gates.
Human Authority
Human operators retain final authority over commits, delegations, and policy exceptions. Governance artifacts define where automation must stop and where human review is mandatory.
Evidence and Replay
Execution should leave durable traces: plans, tool calls, approvals, failures, and recovery points. Replayability is how operators audit what happened — not what the model claims happened.