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The total set of AI systems, models, agents, and embedded AI features in use across an organization at any given time, including sanctioned and shadow systems.
An organization's AI footprint is typically larger than its leaders realize. It includes internally developed AI, procured AI tools, embedded AI features in existing SaaS platforms, AI components inside vendor products, and shadow AI used by individual employees. Mapping and managing the footprint is the first job of any AI governance program - without visibility, no other governance activity can be targeted accurately.
Real world example:
A consulting firm's stated AI inventory lists 12 sanctioned tools. A footprint audit using API traffic analysis surfaces an additional 47 unsanctioned AI services in active use across teams - a 5x expansion of the actual footprint.
