Singulr AI Glossary

AI discovery is the process of finding and cataloging every artificial intelligence model, agent, tool, and service running across an organization's environment. It's the foundational step in AI governance and security — you can't manage, secure, or govern AI systems you don't know exist. AI discovery matters because most enterprises have far more AI running in their environment than they realize. Departments adopt AI tools independently, developers embed AI APIs into applications, employees use AI services through browser extensions and personal accounts, and new agents are deployed without going through central approval processes. The result is an AI landscape that no single team has full visibility into. AI discovery works by scanning an organization's network traffic, application integrations, API calls, authentication logs, and cloud environments to detect AI-related activity. This can include identifying calls to AI model APIs (like OpenAI, Anthropic, or Google), detecting AI agents running on internal infrastructure, cataloging AI plugins and extensions in use across the workforce, and flagging shadow AI tools that haven't been approved by IT. The output is a continuously updated inventory of AI assets — what's deployed, where it runs, who uses it, and what data it touches. For enterprises, AI discovery is the starting point for any AI governance or security program. Regulators increasingly expect organizations to maintain an inventory of their AI systems, and frameworks like the EU AI Act and NIST AI RMF assume this inventory exists as a baseline. Organizations that skip discovery are building governance on incomplete information.