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.