PRISM-AI: A Dual-Stage Neuro-Symbolic Agentic Framework for Privacy Risk Mitigation in LLMs

Abstract

PRISM-AI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> is a neuro-symbolic multi-agent framework designed to mitigate privacy risks during inference by Large Language Models (LLMs). The system integrates a symbolic rule engine based on first-order deontic logic (LogicMP) with a neural agent guided by prompt-engineered constraints aligned with GDPR and Act 25. Each agent fulfills a distinct role, including privacy rule enforcement, input analysis, explanation generation, and user interaction. PRISM-AI introduces a dual-stage privacy control mechanism that evaluates both user prompts and LLM outputs, enabling proactive and reactive filtering of sensitive content. Evaluation across a comprehensive benchmark spanning healthcare, finance, education, and general domains demonstrates that LogicMP achieves $82.5 \%$ accuracy compared to $\mathbf{7 1. 0 \%}$ for LLM-based detection, with $\mathbf{2, 8 0 6} \times$ faster processing and $100 \times$ lower memory usage, while achieving 29.3% precision advantage with perfect precision across Healthcare, Finance, and Education domains. The dual-stage architecture provides $10 \%$ proactive privacy violation prevention, with $100 \%$ of violations caught at the input stage. Legal justification coverage reaches $20 \%$ of blocked cases with automatic GDPR and Act 25 citations. The results underscore the benefits of combining symbolic and neural reasoning within a flexible agentic AI architecture for practical privacy protection. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Interactive demo and source code: https://github.com/SabrineAmri/ prism-ai-demo.git

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