CxO™ DELIVERABLE
AI Governance & Acceptable Use Policy
An AI governance policy is the set of rules, roles and approved tools that tells your people how to use AI to do better work. DAG Tech's fractional chief AI officer writes a governance framework and an AI acceptable use policy that get staff using approved AI quickly, with clear limits they can follow.
Custom proposal · Last updated October 2026
The Short Answer
What Is an AI Governance Policy?
An AI governance policy defines who decides how AI is used in your organization, which tools are approved, what data can go into them, and how staff should use and check AI output. The best reason to have an AI policy is speed. When people do not know what is allowed, most of them avoid AI and a few use whatever free tool they find. Neither helps the business. A clear policy names the approved tools, says which data can go where, and gives practical guidance by role, so staff stop guessing and start using AI in their daily work. That is also how shadow AI shrinks: people switch to approved tools when the approved tools are easy to get and clearly allowed. Governance adds the structure behind the policy: who approves new tools and use cases, when a person must review AI output, how issues are reported, and how the rules keep up with new tools and laws.
In a CxO engagement the policy often comes early, alongside the AI readiness assessment, because staff are already using AI. The policy is paired with role-based training and a simple feedback loop, so it changes behavior instead of sitting in a handbook. It sits alongside your general acceptable use policy, which covers all technology.
At a glance
SPEC SHEET- DELIVERABLE TYPE
- Governance framework and AI acceptable use policy
- ALSO KNOWN AS
- AI policy, AI usage policy, responsible AI policy, AI adoption guidance
- PRACTICE AREA
- Artificial Intelligence (CxO)
- BEST FOR
- Organizations where staff already use AI or a rollout is planned
- WHAT WE NEED
- Current tools list, data types you handle, HR and legal contacts
- NEXT STEP
- Role-based rollout and training
Scope
What We Cover
The pack covers the rules staff follow day to day and the governance that keeps those rules current.
Approved tools list
Which AI tools and features are approved, for which roles and purposes, and how to request a new one.
Data rules
Which data types can be used in which tools, tied to your data classification, with clear examples.
Role-based guidance
Practical do's and don'ts for teams such as sales, finance, HR, operations and IT.
Human review and accountability
When a person must check AI output before it is used, and who is accountable for the result.
Governance roles
Who owns AI decisions, how new tools and use cases are approved, and how exceptions are handled.
Vendor review standard
The questions every AI vendor must answer on data use, training on your data, retention, location and security.
Transparency and fairness
When AI use must be disclosed to customers or staff, and how high-impact uses like hiring decisions are checked for bias.
Incidents and feedback
How to report AI errors or data exposure, and how staff suggest new uses or changes to the rules.
Regulatory alignment
Mapping to the privacy, sector and AI rules that apply to your organization.
The Deliverable
What You Receive
- An AI acceptable use policy written in plain language
- An AI governance framework with roles, approval steps and review cadence
- An approved tools list and a request process for new tools
- A data use matrix showing which data can go into which tools
- An AI vendor review checklist
- A rollout and training plan by role, with a one-page staff quick guide
How It Works
How It Runs
- STEP 01
Kickoff
We confirm goals, data types, applicable laws and who must approve the policy.
- STEP 02
Discover
We inventory current AI use, approved and not, and talk with department leads about what they need.
- STEP 03
Draft
We write the policy, framework and tools list, and review drafts with HR, legal and IT.
- STEP 04
Readout
We present the final pack and the rollout plan, then help launch it with staff.
When to Start
Signs You Need an AI Governance Policy
- Staff ask what AI tools they are allowed to use and get different answers.
- People use free AI tools with company data because nothing else is offered.
- We are rolling out Microsoft 365 Copilot, Google Gemini or ChatGPT Enterprise.
- Customers or auditors ask how we govern AI.
- No one owns approving new AI tools or features.
- We want people to use AI more, and they hold back because the rules are unclear.
Compare
AI Governance Policy vs. General Acceptable Use Policy
| AI Governance & Acceptable Use Policy | Acceptable Use Policy | |
|---|---|---|
| Question it answers | How do we use AI well, with which tools and which data? | How may staff use company technology in general? |
| Output | AI use policy, governance framework, tools list, data matrix, training plan | Organization-wide acceptable use policy for devices, accounts and networks |
| When | When staff use AI or an AI rollout is planned | When onboarding staff or updating IT policies |
| Led by | Fractional chief AI officer | Fractional CTO |
Related
Often Paired With
FAQ
AI Governance & Acceptable Use Policy Questions
What should an AI acceptable use policy include?
Approved tools, rules for which data can go into them, role-based guidance, when a person must review output, who approves new tools, how to report problems, and how the policy is updated. It should be short enough that staff actually read it.
Won't an AI policy slow adoption down?
A clear one speeds it up. Most people hold back when the rules are unclear. Naming approved tools and giving practical examples tells staff they can start, and shows them how to use AI well.
Which frameworks and laws do you align to?
We map the policy to the privacy, sector and AI rules that apply to you. We do not give legal advice, so we review drafts with your counsel.
How much does an AI governance policy cost?
It is scoped in hours at the CxO rate, and your proposal shows the total before any work starts. Scope depends on your size, the data you handle and the laws that apply. Every proposal is custom-scoped.
How do we deal with shadow AI?
Give people a better approved option, make it easy to get, and explain why. The policy then sets which tools are blocked and why. Technical controls, such as blocking unapproved AI sites or data loss prevention rules, can back it up.
How is this different from your blog guide on creating AI policies?
The guide explains the steps to write an AI policy yourself. This deliverable is the finished policy and governance framework, written for your tools, data and laws by a fractional chief AI officer and rolled out with your staff.
Get Started
Request an AI governance policy
Tell us what you need. We scope the hours and send a CxO proposal.
Sent with your request: CxO: AI: AI Governance & Acceptable Use Policy
- 01A scoping call with a fractional executive
- 02Every task, deliverable, hour and cost in one proposal
- 03A roadmap with start date
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About DAG Tech AI Governance & Acceptable Use Policy
An AI governance policy is the set of rules, roles and approved tools that tells your people how to use AI to do better work. DAG Tech’s fractional chief AI officer writes a governance framework and an AI acceptable use policy that get staff using approved AI quickly, with clear limits they can follow.
An AI governance policy defines who decides how AI is used in your organization, which tools are approved, what data can go into them, and how staff should use and check AI output. The best reason to have an AI policy is speed. When people do not know what is allowed, most of them avoid AI and a few use whatever free tool they find. Neither helps the business. A clear policy names the approved tools, says which data can go where, and gives practical guidance by role, so staff stop guessing and start using AI in their daily work. That is also how shadow AI shrinks: people switch to approved tools when the approved tools are easy to get and clearly allowed. Governance adds the structure behind the policy: who approves new tools and use cases, when a person must review AI output, how issues are reported, and how the rules keep up with new tools and laws.