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Impact of Artificial Intelligence and Machine Learning in Cloud Security
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Author(s): I. Eugene Berna (Bannari Amman Institute of Technology, India), K. Vijay (Rajalakshmi Engineering College, India), S. Gnanavel (Department of Computing Technologies, SRM Institute of Science and Technology-Kattankulathur, India)and J. Jeyalakshmi (Amrita VishwaVidhyapeetham, India)
Copyright: 2024
Pages: 25
Source title:
Improving Security, Privacy, and Trust in Cloud Computing
Source Author(s)/Editor(s): Pawan Kumar Goel (Raj Kumar Goel Institute of Technology, India), Hari Mohan Pandey (Bournemouth University, UK), Amit Singhal (Raj Kumar Goel Institute of Technology, India)and Sanyam Agarwal (ACE Group of Colleges, India)
DOI: 10.4018/979-8-3693-1431-9.ch002
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Abstract
The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has had a significant impact on cloud security, as it has in many other sectors. This abstract examines how AI and ML are affecting cloud security, highlighting their major contributions, difficulties, and potential. Traditional approaches to cloud security provide improved threat detection, real-time monitoring, and adaptive defense mechanisms. These technologies are adept at processing enormous volumes of data, allowing them to spot trends, anomalies, and potential dangers that more traditional security measures would miss. In order to quickly identify and take appropriate action in response to unauthorised access, data breaches, and other malicious activities, AI-driven systems can quickly analyse user behaviour, network behaviour, and system logs. It introduces complexity in the form of adversarial assaults, model interpretability, and data privacy issues. For users to trust AI-driven security systems and to comprehend their decision-making processes, openness of these systems is essential.
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