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.

Policy that runsPOLICY THAT RUNSPolicy packWorkflowHuman-at-the-controlOutcome gradeReceiptBASELINEGOVERNPROVE
Policy that runs. A policy pack reaches a workflow, then human-at-the-control and a receipt. Phases: baseline, govern, prove. Decorative illustration.
Answer
Counsel gets a sealed packet: which identity ran, which data class was touched, which model was chosen, who signed, and whether revocation fired. The Analog-to-Digital Sprint puts that packet on one high-risk workflow in fourteen days.

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.
In operation

The baseline pack on a live workflow.

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 id
AR-44c1…cd
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
    Tamper-evident 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 Technology Officer

What does GW Slate™ give a CTO?

A controller they command: custom rails over the estate they already run, policy gates on every route, and Actuality Receipts™ that prove the work. Human-at-the-Control stays in the architecture, so engineering authority scales with the estate.

  • Custom rails over the current stack
  • Policy-gated routing they can audit
  • Actuality Receipts™ on material actions
  • Human-at-the-Control on irreversible steps

Operationalize AI policy on one workflow.

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

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