AI-Driven Secure Coding: Revolutionizing Source Code Defense

Secure coding is a paramount practice in software development, serving to safeguard applications against vulnerabilities and security breaches. Traditionally, this process has relied on manual analysis, a method that can be time-consuming and might overlook potential issues. This paper reviews the integration of Artificial Intelligence (AI), specifically Large Language Models (LLMs), into secure coding practices, presenting an innovative approach to bolster code quality and security. The research explores the dynamic relationship between LLMs and secure coding. LLMs, equipped with advanced natural language processing (NLP) and machine learning (ML) capabilities, possess the unique ability to comprehend code context and accurately identify vulnerabilities. They function as invaluable assistants to developers and security professionals, providing real-time guidance, offering remediation strategies, and amplifying human capabilities. While LLMs offer substantial promise, they are not without challenges. Their interpretive understanding can sometimes result in false positives and negatives. The paper underscores the importance of combining LLM insights with established security practices to establish a comprehensive and robust approach to code security.