LLMs Show Promise for Automating Privacy Compliance in Software Requirements

Turning privacy legislation into concrete software requirements remains one of the thorniest challenges in modern development. A recent investigation published on arXiv explores whether large language models might ease this burden by automating the translation process.

The Compliance Challenge#

Software teams tasked with meeting regulatory requirements like Brazil's General Data Protection Law (LGPD) face a significant hurdle: legal language rarely maps neatly onto technical specifications. Engineers must manually decode abstract legal obligations and recast them as user stories, acceptance criteria, and test scenarios—a labor-intensive and error-prone process. This gap between law and code has only grown more critical as privacy rules proliferate globally.

The LLM Approach#

Researchers led by Cinara Gomes De Melo Carneiro developed a method that feeds LGPD provisions directly into a language model, asking it to automatically generate user stories and acceptance test scenarios. Rather than require software teams to interpret the law themselves, the approach leverages the LLM's ability to synthesize legal text into actionable engineering artifacts.

Results and Implications#

The evaluation indicated strong performance across the generated outputs, suggesting that LLMs can reliably bridge the gap between regulatory text and software design. If validated further, this technique could embed compliance safeguards early in development—reducing costly rework and helping organizations maintain privacy standards from the outset.

The work points toward a future where regulations are machine-readable aids rather than manual translation tasks, though questions remain about generalization beyond the Brazilian context and the role of human review in ensuring accuracy.