Multi-Agent Legal Verifier Systems for Data Transfer Planning

2025-11-14

AI Summary

Addressing the challenges of regulatory compliance verification and low interpretability in AI-driven cross-border data transfers—particularly under privacy laws such as Japan’s Act on the Protection of Personal Information (APPI)—this paper proposes the first multi-agent legal verification framework tailored to APPI Article 16. The framework decomposes compliance assessment into three specialized agents: statutory interpretation, business-context modeling, and risk adjudication, coordinated via a structured synthetic protocol for joint reasoning. Our method integrates statutory semantic parsing, context-sensitive modeling, and hierarchical risk evaluation. Evaluated on 200 amended real-world cases, it achieves an overall accuracy of 72%—a 21-percentage-point improvement over single-agent baselines—with 90% accuracy in identifying explicitly compliant cases and 100% recall in detecting violations. This work significantly enhances the verifiability and accountability of AI systems operating in dynamic, jurisdictionally heterogeneous legal environments.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: AI for Accessibility
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Data transparency and provenance

Abstract

Legal compliance in AI-driven data transfer planning is becoming increasingly critical under stringent privacy regulations such as the Japanese Act on the Protection of Personal Information (APPI). We propose a multi-agent legal verifier that decomposes compliance checking into specialized agents for statutory interpretation, business context evaluation, and risk assessment, coordinated through a structured synthesis protocol. Evaluated on a stratified dataset of 200 Amended APPI Article 16 cases with clearly defined ground truth labels and multiple performance metrics, the system achieves 72% accuracy, which is 21 percentage points higher than a single-agent baseline, including 90% accuracy on clear compliance cases (vs. 16% for the baseline) while maintaining perfect detection of clear violations. While challenges remain in ambiguous scenarios, these results show that domain specialization and coordinated reasoning can meaningfully improve legal AI performance, providing a scalable and regulation-aware framework for trustworthy and interpretable automated compliance verification.

Problem

• Developing multi-agent systems for legal compliance verification in data transfers
• Improving accuracy of automated compliance checking under privacy regulations like APPI
• Addressing challenges in statutory interpretation and risk assessment through specialized agents

Innovation

• Multi-agent legal verifier decomposes compliance checking
• Specialized agents interpret statutes and assess risks
• Structured synthesis protocol coordinates agent reasoning