Topic: Shadow AI

Shadow AI detection is a layered problem. Network and CASB tools reveal which AI destinations traffic flows to. DLP engines inspect what content is leaving. Data-lineage tools trace how information moves once copied or pasted. No single tool answers all three questions, and none can tell you whether the behavior fits an established pattern or signals…

Detecting shadow AI requires more than a one-time discovery scan. It requires a framework that continuously answers four questions: DTEX’s approach to this framework is built on DMAP+, correlating data, metadata, activity, and provenance signals at the endpoint into a single forensic timeline. Step 1: Endpoint-level AI tool discovery  The first step is establishing: what AI…

73% of security professionals now say unauthorized AI use is creating invisible data loss pathways in their organizations, yet 87% have not formally adopted AI into their business strategies, according to the 2026 Ponemon Report. The gap between AI adoption and governance has never been wider, leaving security teams to discover shadow AI only after sensitive data…

Shadow AI is the use of AI tools, models, platforms, or agentic workflows without the appropriate organizational visibility, approval, or governance. Traditional data loss prevention (DLP) remains an important control for identifying and restricting sensitive data movement. However, DLP used alone may not reveal the full context of AI use, especially when activity occurs through…