LLM-based tool auto-generates LGPD-compliant requirements and test scenarios

Translating privacy regulations into concrete software requirements is notoriously difficult, but a new study suggests LLMs can handle much of that burden. The paper, "Simplifying Requirements Engineering in the Context of the LGPD: An LLM-Based Investigation," was written by Cinara Gomes de Melo Carneiro and Renato de Freitas Bulcão Neto, and published on arXiv on August 11, 2026. The researchers focused on Brazil's LGPD (Lei Geral de Proteção de Dados), the country's comprehensive data privacy law, which poses compliance challenges similar to Europe's GDPR. Their proposed approach uses large language models to automatically parse the legal text and generate two key artifacts: User Stories and Acceptance Test Scenarios. This automated pipeline aims to bridge the gap between legal jargon and software engineering practice, removing a major bottleneck in requirements analysis.

According to the abstract, evaluation results demonstrated "high performance," confirming the potential of LLMs to ensure regulatory compliance from the very beginning of software development. Instead of manually interpreting dozens of articles and crafting acceptance criteria, developers could query an LLM to produce traceable, testable requirements aligned with LGPD obligations. While the 12-page paper, which includes 2 figures, does not disclose exact accuracy numbers, the findings reinforce a broader trend of using LLMs for legal-to-technical translation. For teams building products in Brazil or handling Brazilian user data, this could mean faster initial compliance planning and fewer privacy gaps discovered late in development. It also opens the door to similar applications for other regulations, making legal compliance a more integrated and automated part of the engineering lifecycle.