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

Researchers introduce a Privacy Policy Enforcement framework that detects subtle data leakage in RAG systems beyond standard PII filters, using dual one-class density estimators to identify contextual attribute clusters that collectively identify individuals. The T3+OCSVM detector achieves 93%+ AUROC while reducing false positives by 44-55% and maintaining millisecond latency, outperforming traditional supervised approaches.

This research addresses a critical vulnerability in retrieval-augmented generation systems that standard privacy tools frequently overlook. While conventional PII filters catch explicit identifiers like names and social security numbers, they fail to recognize how seemingly innocuous data points—when combined—can collectively expose individual identities. The Privacy Policy Enforcement framework tackles this gap by employing sophisticated anomaly detection rather than simple keyword matching.

The development reflects growing concerns about data leakage in AI systems as enterprises increasingly deploy RAG architectures for sensitive domains including healthcare, finance, and legal services. Traditional approaches struggle because they either rely on rule-based patterns or supervised classifiers that require extensive labeled datasets of privacy violations—both limitations when handling novel contextual combinations.

The proposed solution's technical advantages matter significantly for production deployment. By achieving millisecond latency while maintaining 93%+ AUROC, the framework enables real-time privacy enforcement without degrading user experience. Its superior performance against 14B-parameter LLM judges is particularly noteworthy, as large language models increasingly dominate content moderation tasks. The 44-55 percentage point reduction in false positives directly translates to fewer legitimate queries being blocked, improving usability.

For developers building RAG systems in regulated industries, this methodology provides actionable stress-testing standards. The synthetic data pipeline spanning medicine, finance, and law demonstrates transferability across domains. Organizations implementing strict privacy standards will benefit from having a concrete technical reference for evaluating their own defenses. As regulatory scrutiny intensifies around data protection, tools demonstrating robust contextual privacy detection become increasingly valuable competitive assets.

• →Standard PII filters miss contextual data leakage where non-regulated attribute combinations collectively identify individuals in RAG systems
• →T3+OCSVM detector achieves 93%+ AUROC with 44-55% fewer false positives while maintaining millisecond latency for production deployment
• →Framework outperforms supervised MLP classifiers and 14B-parameter LLM judges by avoiding high abstention rates and latency issues
• →Synthetic data pipeline across medicine, finance, and law provides transferable stress-testing methodology for any domain-specific classifier
• →Addresses critical vulnerability for enterprises deploying RAG in regulated industries increasingly focused on privacy compliance