AI-Driven Cybersecurity Testing: Redefining Quality Engineering Through Adversarial Simulation and Threat Modeling

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Publikašuvnnas:International Journal of Communication Networks and Information Security vol. 17, no. 4 (2025), p. 27-49
Váldodahkki: Kathiresan, Gopinath
Almmustuhtton:
Kohat University of Science and Technology (KUST)
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045 2 |b d20250101  |b d20251231 
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100 1 |a Kathiresan, Gopinath  |u Senior Quality Engineering Manager, CA, USA 
245 1 |a AI-Driven Cybersecurity Testing: Redefining Quality Engineering Through Adversarial Simulation and Threat Modeling 
260 |b Kohat University of Science and Technology (KUST)  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Computer program integrity 
653 |a Machine learning 
653 |a Software 
653 |a Simulation 
653 |a Threats 
653 |a Artificial intelligence 
653 |a Network security 
653 |a Organizations 
653 |a Damping 
653 |a Cybersecurity 
653 |a Engineering 
653 |a Malware 
653 |a Algorithms 
653 |a Automation 
653 |a Threat models 
653 |a Cybercrime 
653 |a Real time 
773 0 |t International Journal of Communication Networks and Information Security  |g vol. 17, no. 4 (2025), p. 27-49 
786 0 |d ProQuest  |t Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3232508531/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3232508531/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3232508531/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch