Solution · Local AI Trust

Local execution is not enough. Local trust has to be proven.

Running inference locally reduces some exposure. It does not, on its own, create trust. A local workflow still needs policy enforcement, identity, human approval, egress controls, outcome grading, and receipts that prove what ran, what was touched, why it was allowed, and what changed.

Prove local trustSee receipts
Policy gate

local_only_v1

v1.2
ALLOWED
  • L-1Inference must run on GW_Edge local.allow
  • L-2No cloud egress for this data class.deny
  • L-3Outbound DNS limited to allowlist.allow
  • L-4Receipt sealed on each material step.allow
Reason: Run executed locally with egress controls; receipts sealed.
Receipt· rcpt_local_…ab
LOCAL · ALLOWED
Runtime
GW_Edge · CUDA · sealed
Egress
blocked · per policy
Approval
policy-only · no human required
Outcome grade
A · safety
Receipt hash
sha256:0x77c1…ab
Answer

Is local AI inherently trustworthy?

Local AI execution can reduce exposure, but it does not automatically create trust. A local workflow still needs policy enforcement, identity, human approval, egress controls, outcome grading, and receipts that prove what ran, what was touched, why it was allowed, and what changed.

What local trust requires

  • Policy enforced at the local runtime.
  • Identity bound per session.
  • Human approval where the policy requires it.
  • Egress controls verified, not assumed.
  • Outcome grading on every run.
  • Receipts sealed locally and anchored.
Why local alone isn't trust

The wins of local don't add up to governed.

  • Local can still exfiltrate.

    Egress controls have to be enforced and proven.

  • Local can still violate brand.

    A local model is not a brand-bound model.

  • Local can still skip approvals.

    Approval is a workflow property, not a runtime property.

  • Local can still be opaque.

    Receipts are what make a local run reviewable.

The trusted local loop

How GlobalizeWe makes local AI inspectable.

  1. 01STEP
    Identity bind
    Per-session identity at the local runtime.
  2. 02PASS
    Policy bind
    Local policy pack signed and loaded.
  3. 03PASS
    Egress fence
    Outbound traffic restricted by allowlist.
  4. 04PASS
    Inference
    Local model, sealed runtime.
  5. 05GATE
    Approval
    Human gate when policy requires.
  6. 06PASS
    Grade
    Outcome grade across dimensions.
  7. 07PASS
    Receipt
    Sealed locally, hash anchored.
  8. 08STEP
    Revocation
    Identity / model / policy revocable at the edge.
What you get

A local stack you can hand to a CISO.

Sealed runtime

Local model + GW Slate sealed against unauthorized changes.

Proven egress

Outbound traffic policy is enforced and tested.

Identity per session

Short-lived identity rotated for each run.

Approval surface

Human approval when policy requires, even on local runs.

Outcome grades

Grades produced locally without leaking content.

Receipts at the edge

Receipts sealed locally and anchored to a trust root.

Buyer-specific examples

Chief Information Security Officer

How do I trust an on-prem AI model?

Bind it to policy, prove egress, rotate identity, require approval where it matters, grade the outcome, seal the receipt. Then run revocation drills.

  • Sealed local runtime
  • Proven egress controls
  • Local receipts anchored to a root
  • Verified revocation

Prove local trust on one workflow.

We run one workflow on a sealed local stack with egress fences and a receipt packet.

Schedule the sprintSee receipts

Keep reading

GW Slate

The control layer on top of local.

Deployment Control

Govern agent deployments.

Enterprise AI Governance

Operational AI controls.

Receipt Rail

Receipts at the edge.