Generative AI (GAI) Use for Cybersecurity Resilience: A Scoping Literature Review

With cyberattacks increasing in volume and number, organizations are increasingly at risk of adverse financial and reputational impacts. Cyber attackers are quick to implement technologies like Generative Artificial Intelligence (GAI) to enhance attacks, while organizations have yet to fully benefit from GAI to improve cybersecurity defenses. This scoping literature review analyzes current research and identifies gaps in the literature about how Generative Artificial Intelligence (GAI) can be used to enhance cybersecurity resilience. The analysis includes an overview of GAI, ethical considerations and challenges, future directions and research opportunities, and a discussion of how this GAI research can be applied.

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Publication Details

Journal
International Journal of Applied Science
Published
2025-03-09
DOI
https://doi.org/10.30560/ijas.v8n2p1
Citations
1
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.48
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article

Generative AI (GAI) Use for Cybersecurity Resilience: A Scoping Literature Review

Jessica Parker
1 citations
International Journal of Applied Science
Anomaly Detection Techniques and Applications
2.48
article

Generative AI (GAI) Use for Cybersecurity Resilience: A Scoping Literature Review

Jessica Parker
article en
1 citations

Abstract

With cyberattacks increasing in volume and number, organizations are increasingly at risk of adverse financial and reputational impacts. Cyber attackers are quick to implement technologies like Generative Artificial Intelligence (GAI) to enhance attacks, while organizations have yet to fully benefit from GAI to improve cybersecurity defenses. This scoping literature review analyzes current research and identifies gaps in the literature about how Generative Artificial Intelligence (GAI) can be used to enhance cybersecurity resilience. The analysis includes an overview of GAI, ethical considerations and challenges, future directions and research opportunities, and a discussion of how this GAI research can be applied.

International Journal of Applied ScienceVol. 8(2)
Marymount University (US)
Openalex Percentile: Top 11%
Anomaly Detection Techniques and Applications
2.48
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