Policy gate
enterprise_baseline_v1
v1.0.3- 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
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.
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.
Operating terms
The controls this hub holds.
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
Baseline pack on a live workflow.
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
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.
Grades and receipts are what make misbehavior visible.
Without them, escaped error is the first thing anyone notices.
Revocation is a fire drill.
If the agent has to be stopped today, no one knows how.
Control set
Eight operational controls every AI workflow inherits.
- 01STEPIdentityShort-lived, per-run.
- 02PASSData classBoundaries enforced at runtime.
- 03PASSModel routingSensitivity drives location.
- 04PASSPolicy gatePer-action verdicts: allow / deny / human.
- 05GATEApprovalRight human, right step.
- 06PASSOutcome gradeQuality as a metric.
- 07PASSReceiptTamper-evident evidence.
- 08STEPRevocationCross-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.
Keep reading
NIST AI RMF
Govern, Map, Measure, Manage on agentic systems.
ISO/IEC 42001
The certifiable AIMS and the audit record.
EU AI Act
GPAI in force; Article 50 live now.
Federal and defense
OMB M-25-21, M-25-22, and DoD Responsible AI.
GW Slate™
The control layer.
Receipt Rail™
Proof for governance.
Deployment Control
Govern agent deployments.
Local AI Trust
Local AI under policy.