BoxGOV + Observability · From Policy to ProofIBMReference processTwo pillars of the control plane
From documented policy to provable control
ControlPlane splits AI governance into two reinforcing processes. GOV turns written policy into automated runtime enforcement. Observability proves every decision end-to-end. Together they convert intention into evidence — the only basis on which control can be proven.
01 · GOV
Move from documented policies to automated runtime enforcement
Operationalize ownership, approvals, risk, and policy decisions — so governance is enforced at runtime, not documented after the fact.
Documented Policy
Written business & regulatory policy — the intent layer. Static and human-readable, but unenforced on its own.
Policy-as-Code
Policy is codified as executable, version-controlled rules with tests and review — no drift between intent and enforcement.
Runtime Enforcement Gate
Every inference call passes the policy gate before execution. Denied calls cost zero and create audit evidence.
Decisions, Approvals & Risk
Ownership, approval authority, and risk tiers drive each decision — allow, deny, or human-escalate.
Feedback & Iteration
Exceptions, drift, and incidents feed back into policy review — governance improves with every observed event.
Ownership
Every policy, agent, and workload has a named owner and an approval authority.
Approvals
High-risk actions require human approval with a captured decision and compensating controls.
Risk Tiers
Tier 1–4 classification drives the depth of review and the breadth of access granted.
Policy Decisions
Allow, deny, or escalate — each decision is deterministic, logged, and reconstructable.
How written policy becomes runtime enforcement — by requirement type
The transformation lifecycle
Domain 1 · Identity & Lifecycle
Every agent has a verifiable identity, a declared owner, and a managed lifecycle from registration to retirement.
02 · Observability
Prove who did what, under which policy, at what risk tier, and what outcome
End-to-end evidence — every AI action is attributable, reconstructable, and regulator-ready.
Who
Identity of the user and agent that initiated the action — joined to department and cost center.
What
The action taken — model, prompt class, tools invoked, tokens consumed, and cost attributed.
Which policy
The policy that governed the call and the risk tier under which it was evaluated.
What outcome
Allowed, denied, or human-escalated — with the approver and full decision context.
End-to-end evidence chain
each stage captures an immutable artifact| Stage | What's captured | Fields |
|---|---|---|
| 1Request | Identity + intent | user_id · agent_id · workflow · prompt_class |
| 2Policy gate | Decision | policy_id · risk_tier · decision (allow / deny / escalate) |
| 3Execution | Runtime | model · tokens · cost · latency · tool calls |
| 4Evidence | Audit record | immutable log entry · approver · retention |
Outcome: regulators and internal audit can reconstruct any AI decision — who, what, which policy, which risk tier, and what outcome — from the immutable evidence store. That is provable control.