AI-Driven Security Posture Management: A Revolutionary Approach to Multi-Cloud Enterprise Security
DOI:
https://doi.org/10.32628/CSEIT25111237Keywords:
AI-Driven Cloud Security, Multi-Cloud Threat Management, Intelligent Security Posture, Predictive Cybersecurity, Machine Learning Threat DetectionAbstract
The landscape of cloud security has undergone a transformative evolution, driven by the complexity of modern digital infrastructures and the escalating sophistication of cyber threats. This comprehensive article explores an innovative AI-driven Cloud Security Posture Management (CSPM) framework that transcends traditional security methodologies. By leveraging advanced machine learning algorithms, neural network architectures, and intelligent automation, the framework offers a proactive, adaptive approach to cybersecurity that addresses the multifaceted challenges of multi-cloud environments. The article demonstrates how intelligent systems can dynamically analyze network interactions, predict potential vulnerabilities, and implement rapid, context-aware response mechanisms, fundamentally reshaping how organizations conceptualize and implement security strategies.
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Gurudatt Kulkarni; Nikita Chavan, "Cloud security challenges," 7th International Conference on Telecommunication Systems, Services, and Applications (TSSA), 2012. Available: https://ieeexplore.ieee.org/document/6366028
Sanhita Dasgupta, "AI-Powered Cybersecurity: Identifying Threats in Digital Banking," 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2023. Available: https://ieeexplore.ieee.org/document/10182479
S. K. Sood, "A combined approach to ensure data security in cloud computing," Journal of Network and Computer Applications, vol. 35, no. 6, pp. 1831-1838, 2012. Available: https://sandeepsood.in/wp-content/uploads/2016/12/1-s2.0-S1084804512001592-main.pdf
Fariba Ghaffari, Hossein Gharaee, "Security considerations and requirements for Cloud computing," 8th International Symposium on Telecommunications (IST), 2016. Available: https://ieeexplore.ieee.org/abstract/document/7881792
Kulvinder Singh, et al., "Cyber Threat Analysis And Prediction Using Machine Learning," IEEE 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021. Available: https://ieeexplore.ieee.org/document/9725445
Paul Olubudo, "Advanced Threat Detection Techniques Using Machine Learning: Exploring the Use of AI and ML in Identifying and Mitigating Advanced Persistent Threats (APTs)," International Journal of Cybersecurity Intelligence & Cybercrime, vol. 7, no. 2, pp. 45-62, 2024. Available: https://www.researchgate.net/publication/380743475_Advanced_Threat_Detection_Techniques_Using_Machine_Learning_Exploring_the_Use_of_AI_and_ML_in_Identifying_and_Mitigating_Advanced_Persistent_Threats_APTs
Akshita Sunerah, "Enhancing Cloud Security with AI Driven Solutions," International Journal of Intelligent Systems And Applications In Engineering. (URL: https://ijisae.org/index.php/IJISAE/article/view/6653/5513)
Samuel Olaiya Afolaranmi, "Multi-Cloud Security Mechanisms For Smart Environments," Master's Thesis, Faculty of Information Technology, Tampere University, Finland, 2023. (URL: https://trepo.tuni.fi/bitstream/handle/123456789/26172/Afolaranmi.pdf)
Srimathi. J, "AI-Enhanced Multi-Cloud Security Management: Ensuring Robust Cybersecurity in Hybrid Cloud Environments," 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 2023. URL: https://www.researchgate.net/publication/379109226_AI-Enhanced_Multi-Cloud_Security_Management_Ensuring_Robust_Cybersecurity_in_Hybrid_Cloud_Environments
Sandeep Chinamanagonda, "Automating Cloud Governance -Organizations automating compliance and governance in the cloud," Multinational Journal of Cybersecurity, vol. 9, no. 3, pp. 112-129, 2024. URL: https://mzjournal.com/index.php/MZCJ/article/view/341/348
James Robertson, et al., "A Cloud-Based Computing Framework for Artificial Intelligence Innovation in Support of Multidomain Operations," IEEE Transactions on Engineering Management ( Volume: 69, Issue: 6, December 2022). URL: https://ieeexplore.ieee.org/abstract/document/9497678
Ms Roopesh, "Advanced Machine Learning Techniques For Cybersecurity: Opportunities And Emerging Challenges," International Journal of Science and Engineering, 2024; 1(4): 93-109. URL: https://www.researchgate.net/publication/386424250_ADVANCED_MACHINE_LEARNING_TECHNIQUES_FOR_CYBERSECURITY_Opportunities_and_Emerging_Challenges
Venkata Tadi, "Quantitative Analysis of AI-Driven Security Measures: Evaluating Effectiveness, Cost-Efficiency, and User Satisfaction Across Diverse Sectors," European Journal of Engineering and Technology Research 11(4):328-343, 2024. URL: https://www.researchgate.net/publication/384935808_Quantitative_Analysis_of_AI-Driven_Security_Measures_Evaluating_Effectiveness_Cost-Efficiency_and_User_Satisfaction_Across_Diverse_Sectors
Taeho Jung, Xiang-Yang Li, et al., "Control Cloud Data Access Privilege and Anonymity With Fully Anonymous Attribute-Based Encryption," IEEE Transactions on Information Forensics and Security ( Volume: 10, Issue: 1, January 2015). (URL: https://ieeexplore.ieee.org/abstract/document/6951492)
Michael Roytman; Ed Bellis, "Modern Vulnerability Management: Predictive Cybersecurity," IEEE Transactions on Information Forensics and Security, vol. 18, no. 6, pp. 678-695, Dec. 2023. (URL: https://ieeexplore.ieee.org/document/10121000)
Dr.A.Shaji George, "Emerging Trends in AI-Driven Cybersecurity: An In-Depth Analysis," Partners UniversalInnovativeResearch Publication(PUIRP), Volume: 02, Issue: 04, July-August 2024. (URL: https://puirp.com/index.php/research/article/view/65/57)
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