An AI-Driven Digital Twin Approach for Multi-Objective Data Center Security and Sustainability Optimization

An AI-enabled Digital Twin framework is proposed for joint optimization of data center security and sustainability in dynamic operational environment. The proposed approach constructs a virtual representation of the data center by combining operational parameters such as energy consumption, temperature, workload, utilization of resources, and security events. Artificial intelligence is used to continuously update and analyze the Digital Twin to find operating configurations that meet multiple objectives such as reducing energy and carbon emissions while reducing security threats and maintaining system performance. Data center operational and security data are used to conduct experimental evaluation under different workload levels and simulated security threat circumstances. The suggested framework is compared with traditional optimization techniques and assessed using energy consumption, carbon emissions, security risk, resource utilization, and performance as key variables. The findings of the experiment show that the proposed framework can dynamically balance security and sustainability goals, resulting in increased energy efficiency and decreased security risk while preserving dependable data center operation.