Citation:

Nitesh Upadhyaya, 2023. "AI-Powered Secure Software Development: The Future of Safe and Efficient Coding ESP Journal of Engineering & Technology Advancements" 3(2): 148-152.

Abstract:

Generative AI tools like GitHub Copilot are transforming software development by providing intelligent code assistance, enhancing developer productivity, and introducing innovative approaches to secure software development. This paper explores the role of generative AI in improving secure coding practices and reducing vulnerabilities in software. It examines how AI-powered coding tools assist developers in adhering to security standards, detecting and mitigating potential vulnerabilities, and fostering secure development practices. The analysis highlights the benefits of integrating generative AI into the Secure Software Development Lifecycle (SSDLC), such as automated code suggestions, real-time feedback on potential security issues, and adherence to secure coding patterns. However, the paper also delves into the limitations and challenges of relying on generative AI, including risks related to biased training data, lack of contextual understanding, and potential for introducing insecure code snippets. Finally, it discusses future directions for enhancing generative AI tools to better address security challenges, emphasizing the need for transparency, explainability, and continuous improvement in their security-focused capabilities. By bridging the gap between generative AI and secure software development, this paper provides actionable insights into leveraging these technologies to build resilient, secure, and high-quality software.

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Keywords:

Secure Software Development, GitHub Copilot, Secure Coding Practices, Vulnerability Mitigation, AI-Assisted Development, Secure Software Development Lifecycle (SSDLC), Code Security Automation, AI-Powered Code Assistance, Cybersecurity in Software Development