AI moved the security decision from the destination to the interaction. An organization no longer has to allow or block a whole AI application when the real risk sits in one prompt, account type, file, output, connector, or tool.

Traditional security acts on destinations, identities, and data patterns. It sees a copilot or a local AI app as a trusted binary, when the risk is the prompt inside it. AI traffic also streams over long-lived sessions and binary protocols that most existing tools cannot decode, and a growing share of AI use happens outside the browser: desktop apps, IDE and CLI coding assistants, AI embedded in SaaS, and agents running on endpoints. What is left is a block-or-allow policy at the destination, applied to tools that need something far more precise.

Aurascape sits inline and decodes the interaction itself: the prompt a person sends, the response the model returns, the mode or capability in play, and the action the AI takes through a connected tool.

The data sheet covers what AI Usage Control is and why destination-level security falls short, the key capabilities of the category, the seven building blocks of precision policy Aurascape reads in an interaction, use cases spanning shadow AI, personal accounts, sensitive data, coding assistants, embedded AI, Microsoft 365 Copilot, and local agents, the AI-native proxy architecture and how it deploys, how Aurascape governs the tool calls agents make, and the evidence that proves effective AI governance, including coverage mapped to the OWASP Top 10 for LLM Applications.