Synthetic Data Usage Policy: Generation and Governance Standards

Policy Rationale

Establishing formal governance for artificial data serves multiple objectives: regulatory compliance with the EU AI Act, operational risk management, stakeholder confidence, and organisational accountability. Without clear policy, AI governance decisions become ad hoc, inconsistent, and difficult to audit.

This policy framework addresses generation methods, validation requirements, privacy guarantees, and acceptable use for synthetic training data. It is designed to be proportionate — organisations with limited AI deployment can implement a lighter version, while organisations with extensive AI portfolios can extend it to cover complex scenarios.

Core Policy Elements

Effective AI policies share critical design characteristics. They define clear scope (which AI systems and activities are covered), assign specific responsibilities to named roles (not abstract functions), establish measurable compliance criteria, and include enforcement mechanisms with proportionate consequences for non-compliance.

The policy should reference EU AI Act requirements directly, creating traceability from regulatory obligations to organisational practices. This traceability is valuable both for internal governance and for demonstrating compliance to regulators. Avoid vague aspirational language — each policy statement should be testable and auditable.

Implementation Strategy

Roll out policy through a structured change management process: stakeholder consultation during development, pilot testing with representative AI systems, training that explains the why behind requirements, and phased implementation with support resources for teams adapting their practices.

Establish clear metrics for policy effectiveness: adoption rates, compliance scores from internal assessments, incident volumes, training completion, and stakeholder feedback. These metrics should be reported to senior leadership regularly to maintain governance visibility and accountability.

Maintenance and Evolution

Schedule annual policy reviews as a minimum, with event-triggered reviews for significant regulatory changes (new implementing acts, harmonised standards), major AI incidents, or substantial changes to the organisation's AI portfolio. Each review should assess fitness for purpose and incorporate lessons learned.

Track the evolving regulatory landscape. The EU AI Act is being supplemented by implementing acts, delegated acts, harmonised standards, and codes of practice. Policies must evolve to reflect these developments. Assign regulatory monitoring responsibility to a specific role or team.

This article is for informational purposes only and does not constitute legal advice. Regulatory requirements change frequently — verify current rules with official sources. Built by Sawai Gyoseishoshi Office, Hiroshima, Japan.