Risk Assessment and Mitigation With Generative AI Models

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Author(s): Siva Raja Sindiramutty (Taylor's University, Malaysia), N. Z. Jhanjhi (Taylor's University, Malaysia), Rehan Akbar (Florida International University, USA), Tariq Rahim Soomro (Institute of Business Management, Karachi, Pakistan)and Mustansar Ali Ghazanfar (University of East London, UK)
Copyright: 2025
Pages: 54
Source title: Utilizing Generative AI for Cyber Defense Strategies
Source Author(s)/Editor(s): Noor Zaman Jhanjhi (Taylor's University, Malaysia)
DOI: 10.4018/979-8-3693-8944-7.ch002

Keywords: Artificial Intelligence / Computer Science & IT / Cyber and Network Security / Information Science Reference

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Abstract

Cybersecurity organisations constantly face a risky environment where threats are present. These dangers can jeopardise information, disrupt business operations, and erode trust. Risk assessment and mitigation strategies are crucial to tackling these challenges effectively. However, traditional approaches often need help to keep pace with the changing landscape of cyber threats that require judgments based on manual analysis. This section delves into how the adoption of AI techniques, like generative adversarial networks (GANs) or variational autoencoders (VAEs), can transform risk assessment methods by simulating scenarios to identify anomalies more efficiently than ever before and predicting potential future risks in real-time through unsupervised learning methods. By integrating threat intelligence into models, the authors improve understanding of contextual factors that help identify abnormal high-risk behaviours.

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