Our small IT team is struggling to keep up with the growing number of AI features appearing across our environment. Microsoft Copilot is the biggest challenge because it has been introduced throughout Microsoft 365 and Windows, with confusing product names and frequent changes that are difficult to review and control. Other vendors are also adding AI capabilities to their products, sometimes enabling them with little or no advance notice.
We operate in a highly regulated industry, so information governance, data handling, and user permissions are critical. Senior management is also still trying to understand what these tools do and how they should be governed. There are only three people on the IT team, and just one has deep Microsoft 365 expertise.
How are other organizations managing this AI-tool overload? Are you using a formal approval process, centralized policies, technical controls, or a risk-based approach to decide what gets reviewed and enabled?
3 Answers
Start with a simple AI governance register. For every product, record whether AI is present, what data it can access, whether it is enabled by default, where processing occurs, and which vendor terms apply. Give each feature a basic risk rating and assign an owner.
Then create a short interim rule: no new AI feature may process company or regulated data until it has been reviewed. That gives management a clear decision point without requiring the IT team to build a perfect program immediately. Ask vendors for change notifications and configuration documentation, and schedule periodic reviews rather than reacting to every announcement individually.
Use a risk-based approach instead of trying to investigate every product feature equally. Identify the systems that handle critical business processes or sensitive data, such as identity, cloud platforms, collaboration tools, ticketing systems, and source control. Apply formal governance to those first, and treat minor AI additions in less important applications as lower priority.
Document the approach in your security and information-governance policies so it aligns with your compliance obligations. For Microsoft 365, central controls such as Conditional Access, Intune policies, tenant settings, and SIEM monitoring can help limit access and detect unwanted data movement. The goal is not necessarily to understand every AI feature immediately, but to control the important entry points and data flows.
Copilot can be useful, but treating it as one simple switch is risky. Microsoft has placed related features across Windows, Microsoft 365, identity, search, and individual applications, so the controls and licensing can vary. If you do not have the resources to enable it safely, temporarily restrict or disable the relevant capabilities, limit access through identity and device policies, and define approved use cases before rolling anything out.
Make management approve those use cases and the acceptable data types. That turns the conversation from “AI is everywhere” into a manageable set of business, privacy, and compliance decisions.
That is the difficulty for us. We cannot simply let users experiment because of our regulatory requirements, and Copilot has appeared in so many parts of the Microsoft ecosystem that reviewing every setting has become a major project.
That makes sense, but our biggest limitation is capacity. We have only three people covering all of IT, and only one of us has substantial Microsoft 365 experience, so even finding and validating the relevant settings takes a lot of time.