This article focuses on the security challenges faced by power data in cloud environments, particularly the risk of data leakage caused by abnormal user behavior. The study proposes an innovative power data security defense technology based on behavioral profiling. This technology constructs a user behavior model through fuzzy C-means clustering algorithm, analyzes real-time access and operation characteristics of power data, forms accurate behavior portraits, and uses machine learning algorithms to identify abnormal behavior patterns. The experimental results show that this technology has extremely high accuracy in detecting normal and abnormal electricity usage behavior, with most data points having an accuracy rate of over 90%, and some even reaching 100%, effectively distinguishing between normal and abnormal behavior. The research conclusion shows that the defense strategy based on behavioral profiling provides a new perspective and efficient means for the security protection of power data, significantly improving the security protection level of power data in cloud environments, and building a solid defense line for the digital transformation of the power industry and the safe operation of smart grids.