Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs) - Falcão, Fabiano Damasceno Sousa; Canedo, Edna Dias (Anais do Simpósio Brasileiro de Qualidade de Software (SBQS); 2024: Anais do XXIII Simpósio Brasileiro de Qualidade de Software, 2024)
Context: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. Goal: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. Method: We conducted a survey with 78 ICT practitioners from five regions of the country. Results: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. Conclusions: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.