Bridging the AI Security Skills Gap: An Approach to Preparing the Cybersecurity Workforce in the AI Era Threats

Sreenivasa Rao Basavala () and Prudhvi Raju Mudunuri ()

International Journal of Innovative Science and Research Technology (IJISRT), 2026, vol. 11, issue 08, 590-596

Abstract: The rapid growth of Artificial Intelligence (AI) is having a profound impact on Cybersecurity. The means by which organizations will protect their networks, systems and applications is undergoing rapid transformation as are how Cyber adversaries design, plan and attack. What was once a purely human-led discipline is increasingly influenced by automated and intelligent technologies, including those that can learn, self-optimize and process enormous volumes of data. Cyberattacks are becoming more sophisticated, more frequent, faster and more covert and the tools and technologies required to counter them are evolving at pace to keep up with the attackers. The information security workforce is quickly getting familiar with new AI-driven technologies. However, there exists a skills gap as most of the current IT staff are trained in more traditional information security practices and not prepared to handle the specific security needs for AI and Deep Learning, analyze potential risks of malicious use of machine learning, and respond to AI powered attacks. Organizations are rapidly deploying new technologies that rely on AI and are in dire need of a skilled workforce to defend them. This paper discusses current challenges and advancements in the use of AI/ML (Machine Learning) in cybersecurity to help address the current cybersecurity skills shortage. It then translates these insights into practical advice for organizations looking to strengthen their cybersecurity workforce through upskilling, cross-functional work, and the use of secure AI within established security processes and procedures.

Keywords: AI Security; Cybersecurity Workforce; Skill Gap; Threat Modelling; MLOps Security; Data AI Security. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2026:08:ijisrt26aug301

DOI: 10.38124/ijisrt/26aug301

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