Who this is for

Built for all machine learning, statistical, and generative models developed internally, fine-tuned, or procured by your organization, including models embedded in products and…

Every section below is included in your download, summaries only shown here, not the policy text.

• —

  Document control

  Owner, version, review cycle, and approval fields ready to customize.

• 1

  Purpose

  Why this policy exists and the outcomes it supports.

• 2

  Scope

  Who and what systems, locations, and activities are in scope.

• 3

  Definitions

  Key terms defined in a table for consistent interpretation.

• 4

  Roles and responsibilities

  Accountability for executives, owners, managers, staff, and IT.

• 5

  Policy requirements

  Numbered, audit-ready controls you can adopt or tailor.

• 6–8

  Exceptions, monitoring, enforcement

  Exception process, review cadence, and consequences of non-compliance.

• 9–10

  Regional compliance and framework mapping

  US, EU, and global obligations plus mapped control themes.

• A–C

  Appendices

  Customization notes, document history, and approval signatures.

Requirement themes addressed, not the verbatim policy language.

• Define release criteria by model risk tier
• Require documented sign-off from model owner, independent validator
• Maintain a model release register linking each production version to validation…
• Prohibit deployment of unapproved models to production via CI/CD gates and change…
• Conduct pre-release review of intended use, limitations, monitoring plan
• Verify training data lineage, license compliance
• Publish internal release notes describing model changes, performance deltas
• Implement staged rollout (canary, shadow, phased) for high-impact models where feasible.
• Re-approve models after material retraining, architecture changes
• Retire superseded model versions securely and update inventory within five business…