A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity
The use of artificial intelligence (AI) technology signifies a significant milestone in the swiftly evolving domain of cybersecurity. This study offers a comprehensive literature review on the role, effect, and future prospects of AI across five critical areas of cybersecurity: threat detection, end...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10836696/ |
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| _version_ | 1832584005884575744 |
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| author | Abdullah Al Siam Moutaz Alazab Albara Awajan Nuruzzaman Faruqui |
| author_facet | Abdullah Al Siam Moutaz Alazab Albara Awajan Nuruzzaman Faruqui |
| author_sort | Abdullah Al Siam |
| collection | DOAJ |
| description | The use of artificial intelligence (AI) technology signifies a significant milestone in the swiftly evolving domain of cybersecurity. This study offers a comprehensive literature review on the role, effect, and future prospects of AI across five critical areas of cybersecurity: threat detection, endpoint security, phishing and fraud detection, network security, and adaptive authentication. The study examines contemporary developments in AI for cybersecurity, highlighting the use of these technologies to enhance security protocols. We examine cutting-edge AI methodologies and principal models across many domains, including machine learning algorithms, deep learning architectures, natural language processing techniques, and anomaly detection algorithms, emphasizing their distinct contributions to enhancing security. Essential comparisons of AI models are presented for each area, outlining their main applications, advantages, and drawbacks. The article examines the assessment criteria and performance outcomes of AI-driven cybersecurity solutions. This report synthesizes previous research while identifying gaps and future prospects, including the integration of emerging AI approaches, the enhancement of real-time threat detection capabilities, and the addressing of changing attack vectors. By providing a holistic view of the current state and future potential of AI in cybersecurity, this paper aims to serve as a foundational reference for researchers and practitioners seeking to leverage AI for robust and adaptive security solutions. |
| format | Article |
| id | doaj-art-e9fcce5b5bac4c718e73dffd1596c465 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e9fcce5b5bac4c718e73dffd1596c4652025-01-28T00:01:09ZengIEEEIEEE Access2169-35362025-01-0113140291405010.1109/ACCESS.2025.352811410836696A Comprehensive Review of AI’s Current Impact and Future Prospects in CybersecurityAbdullah Al Siam0https://orcid.org/0009-0006-6861-6188Moutaz Alazab1https://orcid.org/0000-0003-2823-4776Albara Awajan2Nuruzzaman Faruqui3https://orcid.org/0000-0001-9306-9637Department of Software Engineering, Daffodil International University, Dhaka, BangladeshCybersecurity Department, School of Computing and Data Sciences, Oryx Universal College, Liverpool John Moores University, Doha, QatarDepartment of Intelligent Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, As-Salt, JordanDepartment of Software Engineering, Daffodil International University, Dhaka, BangladeshThe use of artificial intelligence (AI) technology signifies a significant milestone in the swiftly evolving domain of cybersecurity. This study offers a comprehensive literature review on the role, effect, and future prospects of AI across five critical areas of cybersecurity: threat detection, endpoint security, phishing and fraud detection, network security, and adaptive authentication. The study examines contemporary developments in AI for cybersecurity, highlighting the use of these technologies to enhance security protocols. We examine cutting-edge AI methodologies and principal models across many domains, including machine learning algorithms, deep learning architectures, natural language processing techniques, and anomaly detection algorithms, emphasizing their distinct contributions to enhancing security. Essential comparisons of AI models are presented for each area, outlining their main applications, advantages, and drawbacks. The article examines the assessment criteria and performance outcomes of AI-driven cybersecurity solutions. This report synthesizes previous research while identifying gaps and future prospects, including the integration of emerging AI approaches, the enhancement of real-time threat detection capabilities, and the addressing of changing attack vectors. By providing a holistic view of the current state and future potential of AI in cybersecurity, this paper aims to serve as a foundational reference for researchers and practitioners seeking to leverage AI for robust and adaptive security solutions.https://ieeexplore.ieee.org/document/10836696/Artificial intelligencecyber securitycyberattackmachine learningdeep learning |
| spellingShingle | Abdullah Al Siam Moutaz Alazab Albara Awajan Nuruzzaman Faruqui A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity IEEE Access Artificial intelligence cyber security cyberattack machine learning deep learning |
| title | A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity |
| title_full | A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity |
| title_fullStr | A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity |
| title_full_unstemmed | A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity |
| title_short | A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity |
| title_sort | comprehensive review of ai x2019 s current impact and future prospects in cybersecurity |
| topic | Artificial intelligence cyber security cyberattack machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/10836696/ |
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