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eISSN: 2581-9615 || CODEN (USA): WJARAI || Impact Factor: 8.2 || ISSN Approved Journal

Cybersecurity frameworks for AI-enabled leukemia genomic data analysis

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  • Cybersecurity frameworks for AI-enabled leukemia genomic data analysis

Mohammad Kabir Hussain 1, *, Badhon Sutrudhar 2 and Md Shadman Soumik 3

1 Washington university of Science and Technology MBA Healthcare Management.

2 Master of Science in Cyber Security, Department of Information Technology Bay Atlantic University Washington DC, USA.

3 Master of Science in Information Technology Washington University OF Science & Technology.

Research Article

World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879

Article DOI: 10.30574/wjarr.2025.28.1.3501

DOI url: https://doi.org/10.30574/wjarr.2025.28.1.3501

Received on 03 September 2025; revised on 11 October 2025; accepted on 13 October 2025

Genomic data analysis has now been transformed by artificial intelligence (AI), and analysis of leukemia, specifically, has provided revolutionary possibilities of precision medicine. However, there are certain issues that come with this innovation, the major one being the security of sensitive genetic information. The current article focuses on cybersecurity structures that will ensure AI-based analysis of leukemia genomic data is secured. It highlights the need to have a strong encryption, strong data-access policies, and specialized anomaly-detection systems designed for the use of genomic data. In addition, the manuscript outlines the use of blockchain technology to guarantee safety and privacy of genomic information. This paper provides a systematic plan of risk reduction and regulatory adherence by analyzing the current AI models and determining their vulnerability to cyberattacks. The adoption of these systems can make healthcare professionals and researchers have confidence in AI-based leukemia genomic research, which will eventually lead to better treatment results.

Systems; Precision Medicine in Leukemia; Data Privacy and Integrity; Blockchain in Healthcare; Genomic Information Protection; Access Control and Machine Learning

https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-3501.pdf

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Mohammad Kabir Hussain, Badhon Sutrudhar and Md Shadman Soumik. Cybersecurity frameworks for AI-enabled leukemia genomic data analysis. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879. Article DOI: https://doi.org/10.30574/wjarr.2025.28.1.3501.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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