TL;DR Shadow AI refers to AI tools used by employees without approval or oversight. It is rapidly growing across enterprises and creates risks around data exposure, compliance, and governance. Detecting it requires a mix of network monitoring, SaaS analysis, and employee-level visibility into AI usage. In our AI Usage and Benchmark study, we found that […]
What is Shadow AI and How to Detect It in Your Organization Read Post »
AI Risks
TL;DR Auditing AI usage across teams requires combining tool discovery, employee reporting, SaaS analysis, and policy review. Most organizations underestimate usage by 2–5x. A structured audit helps identify risks, enforce governance, and gain visibility into how AI is actually used across departments. Introduction Most enterprises don’t have an AI problem. They have a visibility problem.
How to Audit AI Usage Across Teams (Step-by-Step) Read Post »
AI Execution
TL;DR Most enterprises underestimate how many AI tools employees are using. Discovering them requires a combination of network monitoring, SaaS audits, employee surveys, and policy enforcement. Without visibility, organizations risk data leakage, compliance violations, and uncontrolled AI sprawl. Introduction If your CEO asked today, “How many AI tools are we using across the company?”—most teams
How to Discover All AI Tools Used Across Your Enterprise Read Post »
AI Execution