Validate adherence to legal and regulatory standards (e.g., HIPAA, GDPR, FINRA).
Screens output and the data flowing through a system for regulatory violations: personal data leakage, jurisdictional breaches, copyright exposure, prohibited claims. Combines pattern detection for identifiable data with rules evaluation for jurisdiction-specific obligations.
Any system handling personal, health or financial data, and any output crossing a jurisdictional boundary.
As a substitute for legal review. It catches known patterns; it does not interpret novel regulatory questions.
Detection of personal data leakage must trigger blocking and incident logging, not redaction and continuation — the leak has already occurred at the point of detection if the data reached the model.
Detecting the leak after the data has left. A filter on the output does nothing about what was sent in the prompt, which is where most exposure actually happens.
A healthcare system where remittance advice reaching a model contains patient identifiers, and the control that matters sits on the inbound path rather than the outbound one.