International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-Enhanced Cloud Security: Proactive Threat Detection and Response Mechanisms

Author(s) Santhosh Chitraju Gopal Varma
Country USA
Abstract AI-enhanced cloud security is revolutionizing the way organizations approach cybersecurity by leveraging advanced technologies like machine learning, natural language processing, and deep learning to proactively detect and respond to cyber threats. This paper explores the integration of AI into cloud security systems, highlighting its role in improving threat detection accuracy, reducing response times, and enhancing data protection across cloud environments. The paper also examines various AI-driven tools such as intrusion detection systems, behaviour analytics, and automated threat response mechanisms, which provide real-time defence capabilities. Despite its potential, the adoption of AI in cloud security faces challenges, including data privacy concerns, algorithmic bias, integration with legacy systems, and the high computational costs associated with AI models. Furthermore, ethical, and regulatory challenges complicate the deployment and scalability of AI in cloud security. By addressing these challenges and fostering continuous innovation, organizations can unlock the full potential of AI-enhanced security systems. This paper aims to provide a comprehensive overview of the current state of AI-driven cloud security, identify the key challenges faced by organizations, and offer insights into future developments and opportunities in this rapidly evolving field.
Keywords AI-enhanced cloud security is revolutionizing the way organizations approach cybersecurity by leveraging advanced technologies like machine learning, natural language processing, and deep learning to proactively detect and respond to cyber threats. This paper explores the integration of AI into cloud security systems, highlighting its role in improving threat detection accuracy, reducing response times, and enhancing data protection across cloud environments. The paper also examines various AI-driven tools such as intrusion detection systems, behaviour analytics, and automated threat response mechanisms, which provide real-time defence capabilities. Despite its potential, the adoption of AI in cloud security faces challenges, including data privacy concerns, algorithmic bias, integration with legacy systems, and the high computational costs associated with AI models. Furthermore, ethical, and regulatory challenges complicate the deployment and scalability of AI in cloud security. By addressing these challenges and fostering continuous innovation, organizations can unlock the full potential of AI-enhanced security systems. This paper aims to provide a comprehensive overview of the current state of AI-driven cloud security, identify the key challenges faced by organizations, and offer insights into future developments and opportunities in this rapidly evolving field.
Field Computer Applications
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-03
Cite This AI-Enhanced Cloud Security: Proactive Threat Detection and Response Mechanisms - Santhosh Chitraju Gopal Varma - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.31587
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.31587
Short DOI https://doi.org/g8tgrh
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