Most organizations are deploying AI tools without a governance plan in place. A manager requests ChatGPT integration. A team starts using it for customer drafts. A few months in, someone asks: what are we actually allowed to do with this? By then, your data, your liability, and your compliance posture are already at risk.
The good news: you don’t need weeks of bureaucracy to set up governance. You need a clear roadmap. This AI governance checklist covers the 10 practical steps that should happen before your organization goes live with AI—from policy basics to accountability structures to technical guardrails.
1. Define What You’re Actually Deploying
Start with specificity. “AI” is too broad. Are you deploying Copilot for Microsoft 365? ChatGPT for customer service? An internal LLM for knowledge management? The governance requirements are different for each.
Document:
- What tool or platform you’re using
- Who will have access (department, team size, external vendors)
- What data will go into it (customer data, internal docs, employee information)
- What the tool outputs will be used for (drafting, analysis, decision support, customer-facing content)
This single document becomes your north star for every decision that follows.
2. Classify Your Data and Set Input Boundaries
Not all data should go into generative AI. Customer credit card numbers shouldn’t. Confidential contract terms shouldn’t. Proprietary formulas shouldn’t. But emails, general reports, and non-sensitive knowledge docs probably can.
Create a simple matrix:
- What data types are fair game for the tool?
- What’s strictly off-limits?
- What needs approval or anonymization first?
If you’re using Microsoft Copilot, pay special attention to which systems connect to it—Outlook, Teams, SharePoint, OneDrive. Set those boundaries before you flip the switch.
GCS helps organizations map their sensitive data and enforce these boundaries at the platform level. Contact us to learn how GCS approaches data security with Microsoft controls.
3. Establish a Clear Acceptable Use Policy
Your policy should answer these questions:
- Can employees use AI tools for their job? Yes, no, or only approved tools?
- Can they paste confidential information? Never, sometimes, or in anonymized form?
- Is it okay to use AI outputs directly in client work, or does everything need human review?
- Who’s responsible if something goes wrong?
Make it short—one page is better than 10. People won’t read 10.
Frame it as enablement, not prohibition. “Here’s how we’re using AI to work smarter” beats “here’s what you can’t do.”
4. Assign Clear Accountability
Someone owns this. Not “IT” or “everyone.” One person.
That person’s job:
- Updates the policy as new tools or risks emerge
- Handles exceptions and approvals
- Tracks which teams are using what
- Escalates when someone violates policy
If you’re using Copilot in Microsoft 365, this person interfaces with your Microsoft account team. If you’re using third-party LLMs, they manage API keys and vendor contracts.
5. Build a Process for Approving New Tools
You can’t lock down every AI tool. Employees will want to try new ones. That’s healthy. But you need a gate.
Before a new AI tool is approved for work use:
- Does it meet your data security requirements?
- Where are the servers?
- What’s the vendor’s data retention policy?
- Is there a contract?
- Does it need a department-level approval or IT review?
A simple approval form beats ad-hoc decisions made in Slack.
6. Document How Output Gets Reviewed
AI hallucinates. It makes things up. It gets facts wrong. It can also encode biases from its training data.
Decide on your review standard for each use case:
- Is all output reviewed by a human before it touches a customer? (Usually yes for customer-facing content.)
- Is it reviewed for accuracy or just tone? (Depends on risk.)
- Who does the reviewing, and do they have the expertise to catch errors?
This is non-negotiable if AI is generating external communication or informing business decisions.
7. Create an Audit Trail for Governance Decisions
When six months from now someone asks “wait, did we approve third-party LLMs?”—you need a record.
Keep a simple log:
- What tools are approved?
- Who approved them and when?
- What was the decision rationale?
- When does it get reviewed next?
If you’re using Copilot for Microsoft 365, this is partly handled by Copilot audit logs in Microsoft 365. For other tools, a spreadsheet works fine.
8. Plan for Compliance and Regulation
AI governance isn’t just internal policy. It’s also legal and regulatory.
Questions to ask:
- If you’re in healthcare, does your AI use meet HIPAA requirements? Does your vendor sign a BAA?
- If you’re handling payment data, what does PCI-DSS compliance look like for your AI tool?
- If you’re in a regulated industry (financial services, legal), does your governance plan meet regulatory expectations?
This isn’t about finding loopholes. It’s about knowing which requirements matter to your business.
9. Set Rules for Data Retention and Deletion
When you send data to an AI tool—especially a third-party LLM—what happens to it?
Define:
- Does the vendor retain your data for training? (Most do unless you opt out.)
- Can your employees delete conversations?
- Do you need to delete everything on a schedule?
- What’s your data retention policy if you stop using the tool?
Many organizations choose enterprise LLM platforms specifically because they don’t train on customer data. If that matters to your governance posture, make it explicit.
10. Plan Your First Review and Iteration
Governance isn’t a one-time setup. Plan a review point—30, 60, or 90 days after rollout.
Ask:
- Is anyone violating policy? Why?
- Did we miss any risks?
- Have new tools come onto the market that we should evaluate?
- Are our data boundaries still sensible?
- Do employees actually understand the policy?
Adjust and communicate changes. Governance only works if people know it exists.
Putting Your AI Governance Checklist Into Practice
This AI governance checklist gets you ready to deploy. It doesn’t require a dedicated AI governance officer or months of committee meetings. It requires clarity.
The companies that win with AI aren’t the ones who deployed first. They’re the ones who deployed with a clear understanding of what can and can’t happen with their data, their liability, and their reputation.
GCS helps Austin-area businesses build practical AI governance on the Microsoft stack — from data boundaries to Copilot rollout. Talk to our team about where your organization stands.
FAQ: AI Governance Checklist
What should be included in an AI governance checklist?
At minimum, an AI governance checklist should cover: what tools and data are in scope, an acceptable use policy, clear ownership of the program, a process for approving new tools, human review standards for AI output, an audit trail of decisions, compliance considerations for your industry, data retention rules, and a scheduled review point. The 10 steps above cover each of these in order.
What if we're already using AI tools without governance in place?
Don’t panic. Start with an inventory: what tools are currently being used, who’s using them, and what data is going into them? From there, you can retrofit governance retroactively. Prioritize tools handling sensitive data first. Communicate the new policy clearly to avoid the perception of punishment—frame it as “here’s how we’re doing this going forward.”
How long does it take to set up AI governance?
The checklist itself—writing policies, assigning accountability, setting data boundaries—typically takes 2–4 weeks for a mid-sized organization. Getting buy-in and rolling it out takes another 2–4 weeks. You don’t need it perfect before you start; you need it clear enough to move safely. Plan for iteration.
Do we really need a dedicated person for this?
Not necessarily dedicated full-time, but someone needs to own it. It could be your IT security lead, your compliance officer, or your IT manager. The role requires maybe 5–10 hours per month once it’s established—reviewing approvals, updating policy, handling exceptions, staying aware of regulatory changes. But it needs to be someone’s explicit responsibility.
What if we don't have a legal or compliance team?
Smaller organizations can start with best practices and templates. Review your insurance policy—many cyber insurance policies now include AI governance expectations. Consult with an external legal advisor for the specific compliance questions relevant to your industry. For most SMBs, the governance checklist above is sufficient; you don’t need a team of lawyers to get started.
