AI Discovery is the process of systematically identifying every AI tool, service, API, and model in use within an organization. It is a foundational step in AI governance, enabling organizations to understand their AI footprint before implementing policies and controls.
AI Discovery methods include: network traffic analysis (identifying connections to known AI service domains and APIs), endpoint monitoring (detecting AI applications installed on corporate devices), SSO and OAuth log analysis (identifying AI services authenticated through corporate identity), expense and procurement analysis (finding AI subscriptions and payments), browser extension monitoring (detecting AI browser tools and plugins), API gateway analysis (identifying AI API calls from internal systems), and employee surveys and self-reporting.
The AI Discovery process typically reveals: the number of AI tools in use is far higher than expected, significant Shadow AI usage across departments, sensitive data being processed by unapproved AI services, duplicative AI tool spending across teams, and security gaps where AI tools bypass existing controls.
Organizations should conduct AI Discovery continuously rather than as a one-time exercise, as new AI tools emerge rapidly and employee adoption patterns change constantly. Automated discovery tools integrated with network and identity infrastructure provide the most comprehensive and current visibility.
