Resilient Cloud-Based Enterprise Systems: AI-Enabled Strategies for Cybersecurity and Operational Efficiency
DOI:
https://doi.org/10.21590/Keywords:
Cloud Computing, Artificial Intelligence, Cybersecurity, Enterprise Systems, Resilience, Machine Learning, Predictive Analytics, Threat Detection, Operational Efficiency, AutomationAbstract
The rapid adoption of cloud computing has transformed enterprise systems, offering scalability, flexibility, and cost efficiency. However, this shift has also introduced significant cybersecurity challenges and operational complexities. This study explores the role of Artificial Intelligence (AI) in enhancing the resilience of cloud-based enterprise systems, focusing on strategies that strengthen cybersecurity while improving operational efficiency. AI-driven approaches such as anomaly detection, predictive analytics, automated threat response, and intelligent resource management are examined for their ability to mitigate risks and optimize performance. The paper highlights how machine learning algorithms can proactively identify vulnerabilities, detect real-time threats, and reduce downtime through predictive maintenance. Additionally, AI contributes to operational efficiency by enabling dynamic resource allocation, workload balancing, and cost optimization in cloud environments. Despite these benefits, challenges such as data privacy, algorithm bias, and integration complexity remain critical concerns. This research emphasizes the need for a balanced approach that combines AI capabilities with robust governance frameworks. Ultimately, AI-enabled cloud systems provide a promising pathway toward resilient, secure, and efficient enterprise infrastructures capable of adapting to evolving technological and threat landscapes.
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