From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909676993511424 |
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| author | Kesari, Aniket Breaux, Travis Norton, Tom Santos, Sarah Singhal, Anmol |
| author_facet | Kesari, Aniket Breaux, Travis Norton, Tom Santos, Sarah Singhal, Anmol |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law Kesari, Aniket Breaux, Travis Norton, Tom Santos, Sarah Singhal, Anmol Software Engineering 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. |
| title | From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2507.04185 |