There is the AI program most institutions can describe pretty clearly.
The approved tools. The pilot use cases. The vendor reviews. The governance documents. The version that shows up in a board update and sounds reasonably under control.
And then there is everything else.
The employee using a public AI tool because it helps them move faster. The lender cleaning up narrative language before sending something forward. The operations analyst dropping a block of internal text into a chatbot to get a quick summary. The well-intentioned shortcut that does not feel like a major decision in the moment.
That is usually where the shadow AI conversation starts.
What makes this tricky is that not all of those scenarios carry the same risk. I think that distinction matters more than it gets credit for.
There is a real difference between a free public platform and an enterprise AI environment that comes with contractual privacy protections, administrative controls, and clearer data handling commitments. That matters. It should change how institutions think about approved use.
But it does not make the governance question go away.
An enterprise platform may be a much better answer than a public one. It is still not a blank check. If staff are entering information without clear rules, if leaders do not know which teams are using which tools, if retention settings and access controls have not been thought through, or if outputs are showing up in sensitive workflows without review, the problem has not been solved. It has just moved into a more respectable-looking container.
I think that is the part of the shadow AI conversation that is easiest to miss.
We sometimes frame the issue as if the main choice is between banning public tools and approving private ones. The more useful line is different. It is whether the institution understands where data is going, what terms govern that environment, and what kind of human and operational oversight exists around the workflow.
That is also much closer to how examiners are likely to think about it. Even if a platform offers stronger privacy protections, the institution still has to show that it made a thoughtful decision about use, risk, and accountability.
I do not think the right answer here is prohibition across the board.
Most people are not using AI tools because they are trying to bypass policy. They are using them because the tools are useful. Usually very useful. If the governed path is hard to access, slower than the unofficial one, or too narrow to help with real work, people will keep finding their own way. That is not a character flaw. It is a design signal.
What seems more durable is a clearer line between what belongs nowhere near business work in a public tool, what can live inside an approved enterprise environment, and what should not happen without additional review no matter how private the platform claims to be.
For example, I would make public or free AI tools a hard stop for business work. It is a simpler line, and in practice probably a safer one, than asking employees to make judgment calls in the moment about what is or is not sensitive enough to paste in. If an institution wants AI to support drafting, summarization, research, or other day-to-day work, that should happen in an approved enterprise environment with the right privacy terms and controls around it. And once that work starts influencing member decisions, regulated communications, or higher-stakes internal judgment, the expectations should rise with it. Different controls. Different oversight. Different accountability.
That is not anti-AI. It is just good stewardship.
And for credit unions, stewardship is really the point. Members are trusting institutions with some of the most personal data they have. They are not making fine distinctions between a browser prompt, a vendor platform, and an internal productivity shortcut. If something goes wrong, they experience it as the same thing: the institution did not protect what mattered.
I think that is why shadow AI deserves more attention than it sometimes gets. Not because it is dramatic. Because it is ordinary. It shows up in small decisions, in busy afternoons, in places where people are simply trying to do their jobs a little faster.
That is usually how risk enters the picture now.
How is your institution drawing the line between public AI use, enterprise AI use, and the work that should never touch either without added review?
