NAVIGATING THE DUAL NATURE OF GENERATIVE AI: OPPORTUNITIES AND CHALLENGES IN THE 2024 CYBERSECURITY ARENA

Nikhil John

Authors

Nikhil John Asst. Prof., Department of Computer Science and Engineering, Global Group of Institutes, India. Author

Keywords:

Deep Learning, Machine Learning, Artificial Intelligence, Bias In AI, Fairness In AI, Security Ethics

Abstract

As the digital landscape continues to evolve rapidly, the expanding attack surface presents an increasing challenge for traditional cybersecurity strategies. Generative AI (GenAI) has emerged as a powerful yet complex tool in this context, offering both significant benefits and considerable risks for cybersecurity professionals. This paper aims to explore the dual nature of GenAI, reviewing key research findings related to its applications in threat detection, vulnerability assessment, and security awareness training. The paper delves into the technical foundations of GenAI, including fundamental concepts, deep learning algorithms, and prominent techniques such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Large Language Models (LLMs). It also examines the challenges associated with integrating GenAI into cybersecurity frameworks, such as the potential for sophisticated cyberattacks including personalized phishing, deepfakes used for social engineering, and adversarial machine learning (AML) attacks. Additionally, the paper addresses the complexities of managing the uncertainty and hype surrounding GenAI, along with concerns about potential misuse and bias in GenAI models. Finally, it highlights valuable resources for cybersecurity professionals seeking to stay informed about the evolving relationship between GenAI and cybersecurity, aiming to help them make informed decisions and develop proactive strategies to enhance their organization's security posture while mitigating associated risks.

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