Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2605.17034v1
• Date: Sat, 16 May 2026 15:08:31 GMT
• Title: Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation
• Abstract summary: PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individuals.<n>We introduce a Privacy Policy Enforcement framework using dual one-class density estimators with fused text embeddings and a abstain region for out-of-distribution inputs.<n>Our proposed T3+OCSVM detector, trained on safe and borderline-safe data, achieves a borderline AUROC of 0.93+ while reducing false positives by 44-55 percentage points and maintaining millisecond latency.
• Score: 40.44980598761587
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individuals. We introduce a Privacy Policy Enforcement (PPE) framework using dual one-class density estimators with fused text embeddings and a calibrated abstain region for out-of-distribution inputs. Using an axis-stratified, multi-LLM synthetic data pipeline across medicine, finance, and law, we found that traditional Gaussian Mixture baselines fail on borderline-safe stress tests by focusing on linguistic register rather than content. Our proposed T3+OCSVM detector, trained on safe and borderline-safe data, achieves a borderline AUROC of 0.93+ while reducing false positives by 44-55 percentage points and maintaining millisecond latency. Compared to supervised MLP classifiers or 14B-parameter LLM judges, our framework offers superior operational suitability, as the former suffers from high abstention rates and the latter from latency and calibration issues. This methodology provides a robust stress-testing standard for any synthetic-data-trained classifier.

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