AI-driven cybersecurity in software engineering

Abstract

The rise in complexity of software systems and the rise in sophistication of cyber threats have led to cybersecurity becoming a key issue in software engineering. The conventional security systems are usually unable to capture and react to the current attacks in real time, especially in a large-scale and dynamic environment. The development of Artificial Intelligence (AI) has become a revolutionary solution due to its ability to detect threats intelligently, evaluate vulnerabilities automatically, perform predictive analytics, detect anomalies, and implement adaptive defense mechanisms. This paper will discuss AI-driven cybersecurity in the software engineering field, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security. The study assesses the efficacy of AI-powered technologies in secure coding, threat modeling, penetration testing, reviewing code, malware detection, and responding to an incident. It also examines issues like adversarial attacks, data privacy issues, bias in algorithms, explainability, and integration issues in current development pipelines. Based on a systematic review and comparative framework, the paper shows that AI-based cybersecurity can greatly enhance the ability to proactively defend against threats, decrease reaction time, and increase resilience in the face of new threats. The results highlight the importance of ethical governance, human control, and ongoing updates of models to maximize the advantages of AI in safe software engineering activities. The work can be regarded as a valuable roadmap to researchers and developers, as well as organizations aiming to deploy intelligent cybersecurity approaches to the contemporary software environment.