Exposure now runs in both directions. Sensitive data leaves in the prompt a user sends and arrives in the response the model returns. Traditional Data Loss Prevention (DLP) does not reliably read either with AI-specific context. Pattern-matching tools trip on ordinary nine-digit numbers, text-only tools miss images, audio, and video.

Most exposure is not an attack. It is ordinary authorized work: sensitive data pasted into a chatbot, or an agent returning malicious files, often from a personal AI account. It is also a compliance problem. The data employees send to and receive from AI often includes PII, PHI, PCI, or material nonpublic information, which can implicate HIPAA, GLBA, PCI DSS, GDPR, SEC Regulation S-P, and the EU AI Act depending on the data and jurisdiction.

Aurascape inspects every AI interaction, then acts on it. Policy decisions use identity, entitlement, intent, connection, the full prompt, the response, and the sensitive content itself, not keywords. A three-layer classification engine reads the topic, narrows to the subcategory, then confirms the identifier, the reverse of regex DLP. Each interaction is allowed, coached, redacted, or blocked before exposure.

The solution brief covers stopping source code and IP loss through AI, keeping regulated data out of prompts and responses, screening model output for unsafe content and malicious links, blocking prompt injection and jailbreaks, cutting false-positive noise, and producing defensible evidence of policy adherence for audit.