AI-Driven Cybersecurity Measures for Hybrid Cloud Environments: A Framework for Multi-Cloud Security Management
Keywords:
Artificial Intelligence, Multi-Cloud Security, Threat Assessment, Hybrid Cloud, Autonomous Security, Cybersecurity, Quantum Computing, Real-Time Threat Response, Adaptive Security SystemsAbstract
The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. For hybrid cloud deployment, this mathematical approach maximizes a security metric. Programmers use backslashes to escape special char acters and identify file paths. The objective function optimizes application and data asset security by using AI-enhanced security solutions across all cloud providers, taking their weights into consideration. The conclusion is that hybrid cloud environ ments can benefit greatly from AI-enabled multi-cloud security management. Us ing a combination of mathematical modeling and data-driven AI techniques, we were able to dynamically adjust our security allocations in real-time response to evolving threats, optimize effective resourcing for threat incident response based on continuous risk assessment/prediction, learning from past incidents as well as feedforward modeling. Although the paper studies hypothetical data, it provides a nicely conceptual basis for using empirical data in the real world and this study is an important step towards navigating practical implications in cloud security man agement a sentence.
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