The problemAI creates a new exit point for sensitive data.
Your databases can be encrypted, permissioned, and compliant while the application still sends too much context to a third-party model. The AI call needs its own enforcement boundary.
Protected systemsSource dataRBAC · encryption · access policy
→Application layerContext assemblyPrompts · RAG · tools · records
→External boundaryAI providerWhat is actually allowed to leave?
The enforcement gap01Sensitive data
What is your application allowed to send?
SSNs, dates of birth, emails, phone numbers, payment data, and other sensitive values can travel inside prompts, tool arguments, and model responses. Direct provider calls give security teams no single place to decide where each data type is allowed to go.
02Policy drift
Who decides which provider, model, and spend are acceptable?
AI applications can change providers, models, output limits, streaming behavior, and usage patterns quickly. Without a shared enforcement point, those controls become scattered across application code and provider-specific settings.
03Request + response risk
What happens before and after generation?
Prompt-injection attempts arrive before the provider call. Sensitive data can appear again in model output. If the only security boundary is the model itself, important checks happen too late or in different places.