Practical guide

A practical starting point for AI governance policy work

An AI governance starter policy should set basic expectations before AI usage becomes fragmented across teams. It should be simple enough to adopt, but concrete enough to shape approvals, review, and restricted use.

What matters in practice

Approved and restricted AI use

A strong starting point makes clear where AI can support work and where the business does not want AI making decisions, commitments, or external claims without a person in the loop.

Data handling and confidentiality

Governance starts to matter once confidential information, customer content, source code, internal plans, or regulated data could pass through third-party tooling.

Accountability and review

Someone needs to own tool approval, review standards, and vendor checks. Even a lightweight starter policy should make those defaults explicit.

Useful checklist

  • Approved use cases
  • Restricted data types
  • Human review expectations
  • Tool approval ownership
  • Vendor review considerations

Source references

GuardAxis uses public framework material as reviewer context, not as certification or legal advice.

Review framework boundaries

Related pages

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If you want GuardAxis to turn these policy questions into a structured draft for your business, request a practical walkthrough.

GuardAxis is founder-built and still in an early launch phase. Requests go directly to support@guardaxis.io.