From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law

2025-07-05

AI Summary

Privacy laws designate “consent” as a lawful basis for data processing, yet its translation into software implementations has long suffered from a legal–technical gap and opaque development practices. This paper proposes the first LLM-based, three-step automated framework: (1) legal clause parsing, (2) use-case compliance classification, and (3) technical requirement reconstruction—augmented by human-in-the-loop verification to ensure legal alignment. It establishes the first systematic, end-to-end mapping from privacy regulation text to executable technical specifications, enabling compliance-aware requirements engineering and use-case remediation. Empirical evaluation demonstrates that the LLM effectively identifies and rectifies non-compliant use cases, validating its feasibility for automated compliance tasks; it also reveals persistent limitations in complex legal reasoning. The work introduces a novel paradigm and practical pathway for AI-augmented legal technologization.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Responsible Web: Consent frameworks and practices on the web

Abstract

Privacy law and regulation have turned to "consent" as the legitimate basis for collecting and processing individuals' data. As governments have rushed to enshrine consent requirements in their privacy laws, such as the California Consumer Privacy Act (CCPA), significant challenges remain in understanding how these legal mandates are operationalized in software. The opaque nature of software development processes further complicates this translation. To address this, we explore the use of Large Language Models (LLMs) in requirements engineering to bridge the gap between legal requirements and technical implementation. This study employs a three-step pipeline that involves using an LLM to classify software use cases for compliance, generating LLM modifications for non-compliant cases, and manually validating these changes against legal standards. Our preliminary findings highlight the potential of LLMs in automating compliance tasks, while also revealing limitations in their reasoning capabilities. By benchmarking LLMs against real-world use cases, this research provides insights into leveraging AI-driven solutions to enhance legal compliance of software.

Problem

• Bridging legal consent requirements and technical implementation in software
• Automating compliance tasks using Large Language Models (LLMs)
• Validating AI-generated modifications against legal privacy standards

Innovation

• LLMs classify software use cases for compliance
• LLMs generate modifications for non-compliant cases
• Manual validation ensures legal standards adherence