01
The silent vector: what PII leaks through AI APIs without you knowing
Filtering PII before AI API calls requires two independent layers: an HTTP proxy like Rampart intercepting requests before they leave your network, and a neural NER model like GLiNER2 detecting names, ID numbers, and sensitive data in unstructured text. Combined correctly, they reduce PII exposure to under 2% per request. Implementation time in a standard Docker stack: 4 to 8 hours per integration.
02
Why regex-only filtering covers just 60-70% of the real problem
03
GLiNER2: neural entity recognition that reads context, not just format
04
Rampart: the HTTP proxy that acts before data crosses your perimeter
05
The uncomfortable truth: a well-built filter can create new GDPR risks
Do you have AI integrations in production and aren't sure what personal data is leaving in prompts? We audit your AI pipeline and design the right PII filtering stack for your architecture.
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