Shadow AI Detection: Building Programs from Scratch

The number of AI tools available to employees without formal IT approval has grown rapidly over the past two years. Staff are using AI-powered writing assistants, data summarizers, and other tools without involving security or IT leadership. This is an operational reality in businesses across every major US industry. Here's the thing: existing security infrastructure wasn't designed to detect or respond to unauthorized AI usage. The controls for traditional shadow IT don't directly apply to AI governance. Building an effective detection program means rethinking assumptions about risk. Shadow AI refers to AI tools used within an organization that haven't been approved or monitored by internal functions like IT or security. Shadow AI detection identifies where these tools exist, how they're used, what data they process, and the risks they introduce. Many of these tools are browser-based or embedded within existing platforms. Effective detection programs start by clearly defining what an AI tool is for the organization. Without this, detection efforts can be too broad or too narrow. What's interesting is that many AI tools don't require installation. A browser extension or a web application can transmit sensitive data without triggering traditional software alerts. This matters because it highlights a new and evolving risk landscape for organizations.