AI policy essentials: the policies your organization needs

Start with a few, not forty

Good AI governance doesn't require an exhaustive policy library. It requires a small set of clear, adopted, acknowledged policies that people actually follow. Here are the essentials.

1. Acceptable use of AI

The foundation. Who can use AI, for what, and the ground rules: only approved tools, no regulated data in unapproved tools, a human is accountable for anything published, and report incidents. Everyone should read and acknowledge it.

2. AI data handling & confidentiality

Which data classes may go into which tools. Define your classes (public, internal, confidential, personal, regulated) and the handling rule for each — including "never paste secrets or another party's confidential data into any AI tool."

3. Human oversight & accountability

Set the expectation that consequential decisions keep a human in the loop, define your automation levels, and name an accountable owner for each AI system. (More on meaningful human oversight.)

4. AI procurement & third-party review

How new AI tools get evaluated before adoption — data-use and training terms, sub-processors, retention, security, and required contracts (like a DPA). This is your defense against risky shadow tools.

5. High-risk & prohibited practices

Name the uses that require enhanced governance (decisions about people, sensitive data, high automation) and the ones you don't permit at all. Clarity here prevents the worst outcomes.

Make policies live, not shelfware

A policy only counts if it's published, acknowledged, and versioned:

• Capture who has acknowledged the current version — that's your evidence.
• Re-acknowledge on material changes.
• Review at least annually.

From blank page to adopted

Writing these from scratch is the hard part. AI Assurance Hub ships framework-mapped policy templates you can preview, adopt in a click, tailor, and track acknowledgments against. Explore the live demo or read the NIST AI RMF checklist.