An AI application does not behave one way. In a single session an employee can ask for a summary, request a code review, upload a file, and let the assistant move into agent mode on its own to finish the job. One application and one login, four activities with four different risk profiles. Increasingly the tool picks the mode itself, based on what it reads as the goal of the task rather than what the user turned on.

Traditional security controls answer where traffic went and who sent it. They were not built to read what the AI was asked to do, and that is where AI risk and AI cost both live. It leaves an app-level yes or no as the only option: blocking the application is bad for business, but fully allowing it bypasses security and IT policy.

Aurascape sits inline on an AI-native proxy architecture and decodes the complete AI interaction: prompts, responses, files, code, account type, connectors, and tool calls. It identifies the Intentions in play and evaluates policy with the user, the application, the account type, and the data involved, with streaming preserved through inspection. Enforcement is real time and dynamic: allow, coach, notify, redact, redirect, and block, applied per Intention rather than per application.

The at a glance defines an Intention as a capability, mode, or action available inside an AI application or agent, explains why the set of Intentions differs from one tool to the next, and walks through the control gap that app-level policy leaves open and how to close it. Read the At a Glance to learn more.