Exploring the Role of Artificial Intelligence in Enhancing Security Operations: A Systematic Review

Artificial intelligence (AI) is reshaping Security Operations Centers (SOCs). This systematic literature review analyses AI’s transformative impact across the NIST Cybersecurity Framework. The analysis of 189 papers related to AI use-cases for SOCs shows widespread application of AI for detection, with 65% of studies focusing on it. Yet, it also reveals deficiencies in recovery, the underutilisation of explainable AI models—with 88% of studies relying on non-explainable approaches— the sporadic release of tools as open-source and an over-reliance on proprietary datasets. Common motivations for papers include efficiency, error reduction, and cost savings, with challenges in data reliance, and integration complexity.

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Publication Details

Journal
ACM Computing Surveys
Published
2025-07-18
DOI
https://doi.org/10.1145/3747587
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Exploring the Role of Artificial Intelligence in Enhancing Security Operations: A Systematic Review

Roland Meier, Jacques Klein, Despoina Giarimpampa, Vincent Lenders et al.
ACM Computing Surveys
Network Security and Intrusion Detection
article

Exploring the Role of Artificial Intelligence in Enhancing Security Operations: A Systematic Review

Roland Meier, Jacques Klein, Despoina Giarimpampa, Vincent Lenders, Tegawendé François Bissyandé
article en

Abstract

Artificial intelligence (AI) is reshaping Security Operations Centers (SOCs). This systematic literature review analyses AI’s transformative impact across the NIST Cybersecurity Framework. The analysis of 189 papers related to AI use-cases for SOCs shows widespread application of AI for detection, with 65% of studies focusing on it. Yet, it also reveals deficiencies in recovery, the underutilisation of explainable AI models—with 88% of studies relying on non-explainable approaches— the sporadic release of tools as open-source and an over-reliance on proprietary datasets. Common motivations for papers include efficiency, error reduction, and cost savings, with challenges in data reliance, and integration complexity.

ACM Computing SurveysVol. 58(3)
University of Luxembourg (LU), Cyber Defense Agency (United States) (US)
Openalex Percentile: Top 78%
Network Security and Intrusion Detection
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