AI-Based Threat Modeling for Secure Software Design

Abstract

The introduction of Artificial Intelligence (AI) software design is a revolution in cybersecurity. This paper discusses how AI algorithms can be used to predict the attack surface of a software application at the design stage and thus improve vulnerabilities at the initial stage of the software development life cycle. The analysis of AI potential to detect patterns and anomalies that could be otherwise missed using traditional security methods is conducted using machine learning models. These techniques involve using some AI methods, including supervised and unsupervised learning, on historical software data to predict outliers. The findings indicate that AI-based threat modeling can be an effective approach to predicting vulnerabilities, and it provides more precise and timely information than traditional techniques. Finally, the research points out the significance of constructive countermeasures in the development lifecycle, and how AI may lead to a more secure software design by predicting threats prior to their occurrence. The evidence highlights how AI can be critical in the future of secure software engineering.