Who this is for

Built for all machine learning and statistical models in production or pilot, including third-party models integrated into your organization products and internal analytics.

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.

• Assign a model owner and risk tier (low, medium, high) before development or…
• Document intended use, limitations, training data lineage
• Perform validation appropriate to risk tier
• Implement monitoring for data drift, performance degradation
• Require change control and re-validation for material model updates, retraining
• Maintain rollback and kill-switch procedures for models that pose immediate harm or…
• Segregate duties between model developers and validators for high-risk models where…
• Retain validation artifacts, monitoring logs
• Decommission models securely when retired
• Escalate model incidents affecting customers, safety