A Neuro-Symbolic Multi-Agent Approach to Legal-Cybersecurity Knowledge Integration

2025-10-27

AI Summary

In the interdisciplinary domain of cybersecurity and law, conventional tools fail to model fine-grained semantic relationships among legal cases, statutory provisions, and technical vulnerabilities—hindering effective cross-disciplinary collaboration. To address this, we propose NeSy-MAS, the first neural-symbolic multi-agent system tailored for cyber law. It integrates neural models for semantic alignment across multilingual legal texts and vulnerability databases with symbolic reasoning for cross-modal knowledge fusion and joint inference. The system employs a modular multi-agent architecture, where specialized agents perform legal parsing, vulnerability mapping, logical validation, and cross-lingual alignment. Experiments demonstrate that NeSy-MAS significantly outperforms baseline methods on multilingual legal retrieval, vulnerability compliance assessment, and case-based analogical reasoning. By unifying linguistic, logical, and technical representations, it effectively bridges the knowledge gap between legal scholarship and cybersecurity practice.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Cyber-crime defenses and forensics

Abstract

The growing intersection of cybersecurity and law creates a complex information space where traditional legal research tools struggle to deal with nuanced connections between cases, statutes, and technical vulnerabilities. This knowledge divide hinders collaboration between legal experts and cybersecurity professionals. To address this important gap, this work provides a first step towards intelligent systems capable of navigating the increasingly intricate cyber-legal domain. We demonstrate promising initial results on multilingual tasks.

Problem

• Integrating legal and cybersecurity knowledge across complex information spaces
• Bridging collaboration gaps between legal experts and cybersecurity professionals
• Developing intelligent systems for navigating intricate cyber-legal domains

Innovation

• Neuro-symbolic multi-agent system for legal-cybersecurity integration
• Intelligent system navigating complex cyber-legal domain
• Multi-agent approach bridging legal and cybersecurity knowledge