Solution · Enterprise AI Governance

Policy should not live in a PDF while AI acts in the workflow.

Enterprise AI governance needs more than policy documents. It needs operational controls inside the workflow: identity, data boundaries, model routing, policy gates, human approval, outcome grading, revocation, and receipts that show whether the workflow respected enterprise rules.

Operationalize policySee GW Slate
Policy gate

enterprise_baseline_v1

v1.0.3
ALLOWED
  • E-1PII never leaves data class A.allow
  • E-2Public claims require evidence.human
  • E-3No model fine-tuning on customer data.deny
  • E-4All material actions sealed in a receipt.allow
Reason: All workflows in this scope inherit the enterprise baseline pack.
Receipt· rcpt_enterprise_…cd
ALLOWED
Workflow
Customer summary → CRM
Policy pack
enterprise_baseline_v1
Data class
A · PII present
Approval
Customer Ops · approved
Outcome grade
A · safety + privacy
Receipt hash
sha256:0x44c1…cd
Answer

What is enterprise AI governance?

Enterprise AI governance needs more than policy documents. It needs operational controls inside the workflow: identity, data boundaries, model routing, policy gates, human approval, outcome grading, revocation, and receipts that show whether the workflow respected enterprise rules.

Operational, not aspirational

  • Identity bound to every run.
  • Data boundaries enforced at runtime.
  • Model routing reflects policy.
  • Approvals are scoped to risk.
  • Outcome grades make quality measurable.
  • Receipts make policy auditable.
The governance gap

Policy documents don't enforce themselves.

  • Policy lives in PDFs.

    Useful for committees. Invisible to agents and workflows.

  • Risk reviews end at procurement.

    Once the tool is bought, the workflow is unmonitored.

  • There is no signal for misbehavior.

    Without grades and receipts, the only signal is escaped error.

  • Revocation is a fire drill.

    If the agent has to be stopped today, no one knows how.

The control set

Eight operational controls every AI workflow inherits.

  1. 01STEP
    Identity
    Short-lived, per-run.
  2. 02PASS
    Data class
    Boundaries enforced at runtime.
  3. 03PASS
    Model routing
    Sensitivity drives location.
  4. 04PASS
    Policy gate
    Per-action verdicts: allow / deny / human.
  5. 05GATE
    Approval
    Right human, right step.
  6. 06PASS
    Outcome grade
    Quality as a metric.
  7. 07PASS
    Receipt
    Hash-anchored evidence.
  8. 08STEP
    Revocation
    Cross-system, drilled regularly.
What governance becomes

From policy theater to policy enforcement.

Policy packs as code

Versioned, signed, deployable. Reviewable by humans, readable by the runtime.

Data class enforcement

What can leave which boundary — at runtime, not in a wiki.

Routing matrix

Local, hybrid, or cloud — based on policy, not engineering convenience.

Scoped approvals

Approve only where the policy says — no theater.

Receipt rail

Audit packet for every workflow. SIEM-ready.

Revocation drills

Tested like backups, not assumed.

Buyer-specific examples

Chief Information Officer

How do I make AI policy operational?

Convert policy into policy packs. Bind them at runtime. Use grades and receipts to monitor. Run revocation drills.

  • Policy as packs
  • Runtime enforcement
  • Operational signal (grades + receipts)
  • Drilled revocation

Operationalize AI policy on one workflow.

The Team Control Sprint binds your policy as packs, enforces them at runtime, and proves it with receipts.

Schedule the sprintSee receipts

Keep reading

GW Slate

The control layer.

Receipt Rail

Proof for governance.

Deployment Control

Govern agent deployments.

Local AI Trust

Local AI under policy.