PRODUCT GUIDE

From a source document to protected AI context.

An AI workflow may start with an email, an uploaded document, a support conversation or retrieved knowledge. Privacy depends on where you inspect that content and what your application does with the result.

Protect at an explicit boundary

Your authorized source integration retrieves the content it is permitted to use. PrivoNest then protects supported text or documents. Your application checks the successful result before forwarding the protected copy. Email, Drive and cloud examples describe this custom integration pattern, not native account monitoring.

Wrap the model call

AI Privacy Guard includes adapters for supported model and framework clients. Keep credentials on your backend, verify the installed client version, and test tool outputs and additional model calls separately. For RAG, evaluate retrieval quality as well as privacy when applying protection before indexing or before generation.

Add practical oversight

Saved application policies and scoped keys can govern configured gateway traffic. Application records, vendor assessments, risk notes and readiness evidence support ownership and review. These records do not automatically certify compliance or create a complete AI governance program.

Measure on your actual writing

Local identifiers, native scripts, mixed English and Romanized language can behave differently. Evaluate each relevant group, including difficult negatives and changed value formats. Report missed entities, false detections, wrong labels, residual output values and request failures alongside F1 and latency.

Run the supplied synthetic benchmark locally to find regressions without Railway inference requests. It is a diagnostic baseline; real-client accuracy requires a separate representative, authorized and reviewed evaluation.