PRISM-AI: A Dual-Stage Neuro-Symbolic Agentic Framework for Privacy Risk Mitigation in LLMs
Bibliographic record
Abstract
PRISM-AI1is 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.1Interactive demo and source code: https://github.com/SabrineAmri/ prism-ai-demo.git
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".