Authors :
Dr. Osaro-Mitchell Christoper Osazuwa; Dr. Martha Ozohu Musa
Volume/Issue :
Volume 9 - 2024, Issue 5 - May
Google Scholar :
https://tinyurl.com/34a74b36
Scribd :
https://tinyurl.com/rhnn8h48
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAY1613
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Cyber threats' increasing magnitude and
intricacy require a fundamental change in security
operations. Conventional approaches face difficulties in
keeping up, which exposes organizations to risks. This
paper examines the expanding attack surface: securing AI
and machine learning systems in security operations as a
remedy. A literature review, informed by the Diffusion of
Innovation Theory, investigates how organizations absorb
innovations in this study. The results demonstrate notable
benefits of AI/ML in security, such as superior
identification of threats, improved efficiency through
automation, and optimized management of vulnerabilities.
Nevertheless, achieving successful execution necessitates
meticulous deliberation of obstacles. These tasks
encompass guaranteeing data accuracy, preserving the
capacity to understand how models work, reducing any
potential prejudices in AI/ML models, and resolving
security weaknesses in the systems themselves. The paper
also discusses ethical considerations and emphasizes the
important function of human monitoring. To address these
difficulties, the study recommends prioritizing data
quality, utilizing explainable AI methods, and developing
tactics to mitigate bias. Furthermore, there is a strong
emphasis on using a human-in-the-loop strategy to take
advantage of humans' expertise and machine-learning
capabilities. This study highlights the capacity of artificial
intelligence and machine learning to transform security
operations completely. By confronting the recognized
obstacles, organizations may unleash the genuine potential
of these technologies and establish a stronger and more
proactive security position in response to constantly
changing cyber threats.
Keywords :
Artificial Intelligence, Machine Learning, Security Operations, Cyber Threats, and Data Quality.
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Cyber threats' increasing magnitude and
intricacy require a fundamental change in security
operations. Conventional approaches face difficulties in
keeping up, which exposes organizations to risks. This
paper examines the expanding attack surface: securing AI
and machine learning systems in security operations as a
remedy. A literature review, informed by the Diffusion of
Innovation Theory, investigates how organizations absorb
innovations in this study. The results demonstrate notable
benefits of AI/ML in security, such as superior
identification of threats, improved efficiency through
automation, and optimized management of vulnerabilities.
Nevertheless, achieving successful execution necessitates
meticulous deliberation of obstacles. These tasks
encompass guaranteeing data accuracy, preserving the
capacity to understand how models work, reducing any
potential prejudices in AI/ML models, and resolving
security weaknesses in the systems themselves. The paper
also discusses ethical considerations and emphasizes the
important function of human monitoring. To address these
difficulties, the study recommends prioritizing data
quality, utilizing explainable AI methods, and developing
tactics to mitigate bias. Furthermore, there is a strong
emphasis on using a human-in-the-loop strategy to take
advantage of humans' expertise and machine-learning
capabilities. This study highlights the capacity of artificial
intelligence and machine learning to transform security
operations completely. By confronting the recognized
obstacles, organizations may unleash the genuine potential
of these technologies and establish a stronger and more
proactive security position in response to constantly
changing cyber threats.
Keywords :
Artificial Intelligence, Machine Learning, Security Operations, Cyber Threats, and Data Quality.