Jafrin, Reza (2025) ASSESSING THE EFFECTIVE OF ARTIFICIAL INTELLIGENCE IN PREVENTING CYBER ATTACKS ON BUSINESSES. Journal of Adaptive Learning Technologies, 2 (5). pp. 82-108. ISSN 2997-3902

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Abstract

Modern businesses deploy artificial intelligence (AI) as their central cybersecurity solution because it strengthens threat identification together with response protocols and risk protection operations. A review of AI-supported security measures for threat prevention and detection and threat management is conducted through an examination of Microsoft Security Incident Prediction dataset (2023). Supervised machine learning helps the research to understand threat patterns and test AI detection methods while validating predictive models which decrease security vulnerabilities. Visual data analysis through a combination of Tableau and Python tools demonstrates how artificial intelligence helps detection standards as well as enhances incident reactions and lowers false alarms. This research evaluates AI security frameworks to stop cyber-attacks through their three key features which are better detection accuracy and fewer false alarms together with faster response times. The paper analyzes AI abilities through a quantitative analysis by utilizing the Microsoft Security Incident Prediction dataset. Researchers use Python along with Tableau and computational models and machine learning approaches to assess detection outcomes and response times and false alarms of AI-based systems against normal security protocols. Research studies confirm the great power of AI-based cybersecurity technology to enhance danger identification along with minimizing security breaches and accelerating incident response processes. The adoption of AI in cybersecurity faces major hindrances because of systematic program biases as well as high numbers of error alerts and privacy-related issues. The study emphasizes the need to enhance AI algorithms and combines blockchain security with threat intelligence distribution to achieve regulatory compliance through explainable AI. Operating businesses need to make adaptive AI-driven security frameworks their top priority because cyber threats continue developing to protect their critical assets while securing operational resilience. Through its research outcomes this study presents important information about how AI performs in the cybersecurity space for creating practical strategic guidelines to enhance business cybersecurity systems.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	26 Jun 2025 12:35
Last Modified:	26 Jun 2025 12:35
URI:	http://eprints.umsida.ac.id/id/eprint/16224

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