Catch Advisors
AI Strategy

AI Governance Policy: A Practical Guide for IT Leaders

AI is already inside your company, whether you approved it or not.

Employees are using chatbots to write emails. Sales teams are testing AI note takers. Marketing is using AI design tools. Finance is asking vendors about AI forecasting. Support teams are trying to automate ticket replies.

Some of this is useful. Some of it is risky.

That is why every IT Director and CIO needs an AI governance policy. Not a 40-page legal document that no one reads. A simple, practical policy that tells people what is allowed, what is not allowed, and how the business will evaluate AI tools before they become part of the stack.

Good AI governance is not about saying no to everything. It is about helping the company use AI safely, clearly, and with the right guardrails.

Why AI Governance Matters Now

For years, IT leaders dealt with shadow IT. A department bought a SaaS tool with a credit card, uploaded company data, and told IT after the fact.

AI makes that problem bigger.

With normal SaaS tools, the risk was often access, cost, and vendor sprawl. With AI tools, the risk can include sensitive data exposure, poor output quality, copyright issues, compliance problems, and decisions made from incorrect information.

A user can paste customer records into a public AI tool in seconds. A team can connect an AI assistant to a shared drive without understanding the permission model. A vendor can add AI features to an existing product and change how your data is processed.

None of this means AI should be banned. It means AI needs structure.

An AI governance policy gives your company a shared set of rules. It helps employees move faster because they know the boundaries. It helps IT avoid becoming the department of last-minute cleanup. And it gives leadership confidence that AI adoption is not turning into a hidden risk.

Start With a Clear Purpose

The first mistake many companies make is writing an AI policy that sounds like it was built only to protect the company from lawsuits. That matters, but it is not enough.

Your policy should start with a clear purpose:

  • We want to use AI to improve productivity, service quality, and decision-making.
  • We will protect customer, employee, and company data.
  • We will verify AI output before using it in important business decisions.
  • We will evaluate AI tools before connecting them to company systems.

This tone matters. If the policy feels like a ban, employees will work around it.

A good opening statement might be:

“Our company supports responsible use of AI tools. Employees may use approved AI tools to improve their work, but they must protect confidential data, verify outputs, and follow the review process before using new AI software for business purposes.”

That is simple. People can understand it. Legal can improve it later, but IT should help make sure the policy stays usable.

Define What Counts as AI

Many employees do not know whether a tool is an AI tool. They may think AI only means ChatGPT or Gemini. In reality, AI is now built into many products they already use.

Your policy should define AI in plain language.

For example:

“AI tools include software that can generate text, images, audio, code, analysis, recommendations, predictions, summaries, or automated responses based on user input or company data.”

This covers standalone tools and AI features inside existing platforms.

It is also helpful to separate AI use into categories:

Low-risk use: Drafting internal notes, summarizing non-sensitive public information, brainstorming ideas, or rewording general content.

Medium-risk use: Analyzing internal documents, summarizing meeting notes, drafting customer-facing content, or working with business data.

High-risk use: Handling regulated data, customer records, financial decisions, legal content, hiring decisions, security alerts, or anything connected to production systems.

This helps users understand that not all AI use has the same risk. It also helps IT review requests faster.

Set Data Rules Everyone Can Follow

Data rules are the heart of AI governance.

The policy should clearly say what employees can and cannot enter into AI tools. Avoid vague phrases like “use good judgment.” People need examples.

A practical rule might be:

“Do not enter confidential, sensitive, regulated, customer, employee, financial, security, or proprietary company data into any AI tool unless the tool has been approved for that type of data.”

Then give examples of restricted data:

  • Customer names, contracts, or account details
  • Employee records or HR information
  • Financial reports before public release
  • Source code or system architecture diagrams
  • Security logs, incident details, or vulnerability reports
  • Legal documents or merger and acquisition information
  • Passwords, tokens, API keys, or credentials

This does not mean employees can never use AI with company data. It means they need to use approved tools with the right protections.

The policy should also explain approved data handling. For example, some enterprise AI tools may be allowed for internal documents because the company has reviewed the contract, retention terms, access controls, and security model.

Make the safe path obvious. If the only message is “do not do this,” users will find another way.

Require Human Review for Important Outputs

AI can be useful, but it can also be wrong. It can make up facts. It can misunderstand context. It can produce confident answers that sound right and are not.

Your governance policy should require human review before AI output is used in important work.

This is especially important for:

  • Customer-facing communication
  • Legal or compliance materials
  • Security findings
  • Financial analysis
  • Hiring or performance decisions
  • Technical recommendations
  • Executive reporting

The policy should make one point very clear: AI output is not automatically true.

A simple rule works well:

“Employees are responsible for reviewing and verifying AI-generated output before using it for business decisions, customer communication, or operational actions.”

This keeps accountability with the human user. It also helps prevent a common failure mode where someone says, “the AI told me to do it.”

For high-risk areas, require a second review. For example, AI-generated security recommendations should be reviewed by a qualified security team member before action. AI-generated legal language should go through legal review. AI-generated financial analysis should be checked by finance.

Create an AI Tool Approval Process

Employees need a clear way to request new AI tools. If the process is slow or confusing, they will bypass it.

Your approval process should answer five questions:

  1. What business problem does the tool solve?
  2. What data will the tool access or process?
  3. Who will use it?
  4. How will success be measured?
  5. What contract, security, and compliance risks need review?

For many mid-market companies, a simple intake form is enough. The form should collect the vendor name, use case, data types, requested users, integrations, cost, and owner.

Then route the request based on risk.

Low-risk tools may only need IT review. Medium-risk tools may need IT, security, and procurement. High-risk tools may need legal, compliance, finance, and executive approval.

Do not make every request go through the same heavy process. That will slow the business down and create frustration. Use risk tiers so small, safe experiments can move quickly while serious tools get serious review.

Watch for AI Features in Existing Vendors

One of the easiest risks to miss is AI being added to tools you already own.

A vendor may launch an AI assistant, meeting summary feature, ticket recommendation engine, or analytics add-on inside an existing platform. Business users may enable it without realizing that data processing terms have changed.

Your policy should require review before enabling new AI features, even when the vendor is already approved.

Ask these questions before turning on the feature:

  • What data does the AI feature access?
  • Is data used to train or improve vendor models?
  • Can training be disabled?
  • Where is data stored and processed?
  • Does the feature respect existing permissions?
  • Can admins control who can use it?
  • Is there logging and audit history?
  • What happens to data when the feature is disabled?

This is not just a security issue. It is also a contract issue. The AI feature may come with new pricing, new terms, or new usage limits.

IT should partner with procurement and legal so AI features do not slip into the stack unnoticed.

Build an Approved AI Tool List

Employees should not have to guess which tools are allowed.

Create a simple approved AI tool list. Include the tool name, approved use cases, data rules, owner, and support contact.

For example:

  • Microsoft Copilot: Approved for internal productivity use with existing Microsoft 365 permissions. Do not use for regulated data unless approved by compliance.
  • AI meeting assistant: Approved for internal meetings only. Do not use for customer calls without notice and consent.
  • Public chatbot: Approved for public information and general drafting only. Do not enter company or customer data.

Keep this list visible. Put it in your intranet, IT portal, or knowledge base. Update it as new tools are reviewed.

Also create a short list of prohibited uses. Examples may include entering credentials, generating final legal advice, making automated employment decisions, or using unapproved tools with customer data.

The goal is clarity. People should know what they can use today and how to request something new.

Assign Owners and Review the Policy Often

AI governance is not a one-time project. The tools are changing too quickly.

Assign clear ownership. IT may own the technical review. Security may own risk review. Legal may own contract language. HR may own employee guidance. Finance may own budget controls. Executive leadership should sponsor the overall policy.

Then set a review rhythm. At minimum, review the policy every six months. In fast-moving environments, review it quarterly.

Track basic metrics like approved tools, denied tools, AI spend, active users, policy exceptions, and business outcomes. This turns AI governance from a document into an operating system.

The Bottom Line

AI governance should help the business move faster, not freeze it in place.

The companies that win with AI will not be the ones that let every department do whatever it wants. They also will not be the ones that block every new tool. They will be the ones that create clear rules, approve good use cases quickly, protect sensitive data, and keep humans accountable for results.

Start simple. Define allowed use. Protect data. Review tools before they connect to your systems. Keep an approved list. Update the policy often.

If you need a vendor-neutral view of your AI stack, security risk, or buying process, Catch Advisors can help you evaluate options before you sign. Visit catchadvisors.com to start the conversation.