Multi-Agent Legal Verifier Systems for Data Transfer Planning

TL;DR

This paper introduces a multi-agent system for legal compliance verification in AI data transfer planning, significantly improving accuracy over single-agent approaches by specialized reasoning and coordination.

Contribution

It presents a novel multi-agent framework that decomposes legal compliance checking into specialized, coordinated agents, enhancing accuracy and interpretability in AI-driven data transfer planning.

Findings

Achieves 72% overall accuracy, outperforming baseline by 21 percentage points.

Reaches 90% accuracy on clear compliance cases, with perfect violation detection.

Demonstrates the effectiveness of domain-specific agents in legal AI applications.

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.