The global energy sector is undergoing a profound digital transformation driven by growing demands for sustainability, efficiency, and resilience. The shift from traditional power grids to smart, decentralized energy systems has accelerated the adoption of artificial intelligence (AI)-enabled Energy Management Systems (EMS). While these technologies offer significant benefits, they also introduce new data security and governance challenges that must be addressed to ensure trustworthy and resilient energy infrastructure. This study adopts a Design Science Research (DSR) approach to investigate security issues associated with the development and implementation of AI-enabled EMS. Through an analysis of academic and practice-oriented literature, a conceptual framework was developed that takes a lifecycle perspective, tracing data flows through interconnected phases of data generation , sensing , model development , and deployment. Based on the identified challenges, four design principles were formulated to
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