What Is Shadow AI and How Does It Differ from Shadow IT?
In the technology world over the last couple of years, terminology called shadow IT has emerged. Shadow IT is the practice of employees or team members going out and choosing to use different cloud-based or software-as-a-service (SaaS) applications based on what they want to accomplish on a given day.
Years ago, corporate organizations made purchasing decisions on applications for their entire organization, negotiating contracts that included a set number of licenses for software installed locally on machines. As cloud and SaaS applications became more prevalent, it became extremely easy for employees to research and select their own tools without much corporate oversight or governance.
Shadow AI is the natural extension of that pattern. Your team members are going out, evaluating, and deciding which AI tools they think will serve their needs best. The difference is that the risk is significantly higher than it was with traditional applications.
Why Is Shadow AI a Risk to Your Organization?
When employees choose AI tools on their own, those tools may not be appropriate for business use. They may rely on public learning models, meaning data entered could be used to train models accessible to others. In some cases, the tools may not even be legitimate. They could be malicious front ends designed to capture your organization's information.
When end users make those decisions without governance or oversight, the organization is exposed. That is the core risk behind the term shadow AI.
How Do You Control Shadow AI in Your Organization?
The first step is to publish an acceptable use policy specifically for AI. That policy needs to clearly state that any AI tool evaluation or usage must be approved and sanctioned by the organization. Include it in your employee handbook, require new hires to agree to it during onboarding, and send an updated policy to all existing employees for individual signature.
How Should AI Tools Be Evaluated and Approved?
Once an acceptable use policy is in place, you need a formal process for AI applications to be vetted, evaluated, and approved. That process should involve multiple reviewers who examine the standards around each tool, what data is being shared, how it is shared, and whether it feeds into public learning models. The outcome should be a clear, approved list with defined guidelines on what can be used, how, and when.
Where Should You Start If You Are Unsure What Is Already in Use?
Start by auditing what AI and SaaS tools are currently in use, by whom, and for what purpose. This can be a larger task than expected because you cannot always monitor what employees are doing on their own. A full inventory covering both shadow AI and shadow IT gives you the baseline you need to act.
Once that inventory is in place, make sure your team understands and agrees with the usage guidelines and any restrictions. Then confirm that all employees are clearly aware of the requirements. Taking these steps moves your organization toward safer and more compliant AI usage.