AI-driven Predictive Analytics for Enhancing Cybersecurity in a Post-pandemic World: a Business Strategy Approach

Due to heightened technological advancement that has been ignited by the COVID-19 pandemic, the use of technology in running businesses has surged and therefore making businesses prone to cybersecurity threats. The present paper explores the potential of AI as a tool for advancing cybersecurity in the world after a pandemic, and how business organizations can organize its application for protection against new emerging forms of threats. The research draws on secondary industry data and primary case studies of the businesses with AI-based cybersecurity in place. The outcome demonstrates that emerging AI technologies, including machine learning and neural networks, have cut threat detection time by 40% and enhanced the precision of recognizing potential security threats by 30%, beyond conventional cybersecurity solutions. The new and distinct feature of this paper is to elucidate that new threats can be predicted with the help of AI, while there is a continuous need for new training for AI in relation to cyber threats. Drawing from the literature, this paper, therefore, posits that incorporation of AI-based predictive analytics into business cybersecurity models and systems is not only vital for the defense of data and infrastructure against hackers but equally important for the achievement of sustainable, long-term business continuity during the current day, age of technology and globalization.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2024-10-05
DOI
https://doi.org/10.36948/ijfmr.2024.v06i05.28493
Citations
4
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.40
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article

AI-driven Predictive Analytics for Enhancing Cybersecurity in a Post-pandemic World: a Business Strategy Approach

Tariqul Islam, A Khan, S A Mohaiminul Islam -, Md Shadikul Bari - et al.
4 citations
International Journal For Multidisciplinary Research
Network Security and Intrusion Detection
1.40
article

AI-driven Predictive Analytics for Enhancing Cybersecurity in a Post-pandemic World: a Business Strategy Approach

Tariqul Islam, A Khan, S A Mohaiminul Islam -, Md Shadikul Bari -, Rakesh Paul -, Ankur Sarkar -
article en
4 citations

Abstract

Due to heightened technological advancement that has been ignited by the COVID-19 pandemic, the use of technology in running businesses has surged and therefore making businesses prone to cybersecurity threats. The present paper explores the potential of AI as a tool for advancing cybersecurity in the world after a pandemic, and how business organizations can organize its application for protection against new emerging forms of threats. The research draws on secondary industry data and primary case studies of the businesses with AI-based cybersecurity in place. The outcome demonstrates that emerging AI technologies, including machine learning and neural networks, have cut threat detection time by 40% and enhanced the precision of recognizing potential security threats by 30%, beyond conventional cybersecurity solutions. The new and distinct feature of this paper is to elucidate that new threats can be predicted with the help of AI, while there is a continuous need for new training for AI in relation to cyber threats. Drawing from the literature, this paper, therefore, posits that incorporation of AI-based predictive analytics into business cybersecurity models and systems is not only vital for the defense of data and infrastructure against hackers but equally important for the achievement of sustainable, long-term business continuity during the current day, age of technology and globalization.

International Journal For Multidisciplinary ResearchVol. 6(5)
Industry, innovation and infrastructure
Openalex Percentile: Top 17%
Network Security and Intrusion Detection
1.40
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