Harnessing Generative AI for Anomaly Detection in Distributed Cloud Databases

Abstract

Due to the integral complex structures and distributed architecture of cloud databases, such databases are now easily prone to numerous forms of anomalies that make operational continuity and data integrity difficult. These challenges are aggravated by the environment that the cloud databases are placed in: dynamic and growing large-scale; this makes the simple anomaly detection not effective for real-time systems. The purpose of this research is to propose a new model for anomaly identification inside the generative AI class. The model would learn the subtle changes in data patterns and system behavior by utilizing the characteristics of VAEs. This way, the approach presents a proactive and efficient mechanism of anomaly since it involves the modeling of normal operation and identifying a variation. The effectiveness of the proposed model is very high; hence it can be easily implemented for real-time monitoring and mitigation for cloud services. The analysis of the obtained results points out that this solution not only improves the scalability and fault tolerance of the distributed cloud databases, but also increases the security characteristics of the databases while focusing on the important problem of the intelligent and self-tuning anomaly detection in the modern cloud systems.