AI-Driven Cybersecurity Testing: Redefining Quality Engineering Through Adversarial Simulation and Threat Modeling
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| Publicado en: | International Journal of Communication Networks and Information Security vol. 17, no. 4 (2025), p. 27-49 |
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Kohat University of Science and Technology (KUST)
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| Resumen: | Artificial intelligence (Al) is rewriting the book on how organizations do security testing, threat modeling, and quality engineering in the rapidly changing world of cybersecurity. Traditional methods of defense against cyberattacks are becoming insufficient as cyberattacks are becoming more complex and numerous. With its implementation, Al-powered cybersecurity testing brings a paradigm shift as it provides real-time threat detection, automated vulnerability assessment, and proactive defense mechanisms using machine learning and data analytics. This article explores how Al can be integrated into cybersecurity frameworks, especially for adversarial simulation and Al to supplement threat modeling. This explains how these superior methodologies pinpoint system vulnerabilities and emulate actual penetrations to assist organizations in avoiding and damping off agreeable breaches. A further discussion on how quality engineering contributes to modern cybersecurity sounds off and how Al-powered testing reinforces the resilience and integrity of software and systems during the development lifecycle. The article also explores the tools and technologies that enable Al-driven security and compares them as a basis for selecting implementation methods for enterprises. Implementation strategies are provided that are practical, as well as workforce training requirements and common organizational challenges encountered with evidence and ways of overcoming them. We analyze the ethical implications of providing transparency and fairness in decisions and propose responsible Al governance. |
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| ISSN: | 2073-607X 2076-0930 |
| Fuente: | Science Database |