Data Privacy and Security Risks in AI-Based Code Understanding
Samhita Adhyapak · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: The integration of artificial intelligence (AI) into software development has introduced significant privacy and security challenges. AI-based code understanding systems, such as OpenAI Codex and GitHub Copilot, have been found to generate code snippets containing sensitive information, leading to potential unauthorized access and misuse. This study aims to provide a comprehensive understanding of the privacy and security risks associated with these systems and propose effective mitigation strategies. The literature review examines the foundational principles and techniques behind AI-based code understanding systems, including machine learning algorithms, training datasets, and code analysis methods. The assessment of risks involves analysing scholarly articles, industry reports, and case studies that discuss the privacy and security risks inherent in these systems