AI and Cloud Computing in the Energy Industry: Impact on Data Security, Scalability, and Integration Challenges

The objective of this study is to analyze and evaluate the role of artificial intelligence (AI) and cloud computing in transforming the energy industry, with a focus on their impact on data security, scalability, and system integration. The rapid integration of these technologies is reshaping the energy industry by driving digital transformation, optimizing operations, and enabling data-driven decision-making. The paper is driven with mixed-research methods categorizing reviewed materials of fifty-six (56) into five (5) distinct categories, empirical/experimental, review/literature-based, theorical/conceptual, industry/technical report and online articles/experts’ commentaries. In the study a total number of thirty-two (32) among the reviewed literatures on impact on data security has 12, scalability span ten (10) literatures, whereas integration challenges take eleven (11) materials in the study. The article highlights the challenges of safeguarding sensitive energy data in distributed environments, managing scalability demands in response to increasing data volumes, and addressing interoperability issues between legacy systems and modern cloud-based architectures. Through a comprehensive analysis, the study underscores the critical need for robust security frameworks, scalable cloud strategies, and seamless integration models to ensure resilience and sustainability in the energy sector. The findings emphasize that while AI and cloud computing present transformative opportunities, their successful adoption depends on effectively mitigating risks and aligning technological innovation with industry-specific regulatory and operational requirements.