Multi-Agent Legal Verifier Systems for Data Transfer Planning

TL;DR

This work addresses automated verification of APPI Article 16 compliance in data transfer planning by introducing a decomposed multi-agent verifier with four specialized agents (Legal Analyst, Context Analyzer, Risk Assessor, Coordinator) coordinated through a structured protocol. The authors formalize the problem, propose a V(a_i)=C(L(a_i),X(a_i),R(a_i)) framework, and validate it on a 200-case dataset, reporting a rise from $0.51$ to $0.72$ in accuracy and notable gains in unambiguous categories (e.g., Clear Compliance) while maintaining high precision and improved confidence calibration. The results demonstrate that domain specialization and cross-agent synthesis substantially improve legal AI performance, offering a scalable and interpretable approach to automated regulatory compliance verification. Despite a notable processing-time overhead, the method provides a practical front-line filter for data-transfer compliance and sets the stage for integrating legal precedent databases and dynamic agent specialization in future work.

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.

Figures (4)

• Figure 1: Overall Performance Comparison between Single Agent and Multi-Agent approaches across all evaluation metrics. The multi-agent system shows substantial improvements in accuracy, recall, and F1-score.
• Figure 2: Performance Heatmap by Compliance Category. The visualization clearly shows the multi-agent system's superior performance, particularly in Clear Compliance scenarios.
• Figure 3: Confidence vs Accuracy Analysis for Single Agent and Multi-Agent Systems. The multi-agent system shows better confidence calibration with more reliable confidence scores.
• Figure 4: Processing Time Comparison showing the computational overhead of the multi-agent approach. While the multi-agent system requires 6.67x more processing time, this may be acceptable for high-stakes compliance decisions.

Theorems & Definitions (2)

• Definition 1: Data Transfer Action
• Definition 2: Compliance Status