An Intelligent Risk Assessment Methodology for the Full Lifecycle Security of Data

With the development of Internet of Things and artificial intelligence, large amounts of data exist in our daily life. In view of the limitations in current data security risk assessment research, this paper puts forward an intelligent data security risk assessment method based on an attention mechanism that spans the entire data lifecycle. The initial step involves formulating a security-risk evaluation index that spans all phases of the data lifecycle. By constructing a symmetric mapping of subjective and objective weights using the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM), both expert judgment and objective data are comprehensively considered to scientifically determine the weights of various risk indicators, thereby enhancing the rationality and objectivity of the assessment framework. Next, the fuzzy comprehensive evaluation method is used to label the risk level of the data, providing an essential basis for subsequent model training. Finally, leveraging the structurally symmetric attention mechanism, we design and train a neural network model for data security risk assessment, enabling automatic capture of complex features and nonlinear correlations within the data for more precise and accurate risk evaluations. The proposed risk assessment approach embodies symmetry in both the determination of indicator weights and the design of the neural network architecture. Experimental results indicate that our proposed method achieves high assessment accuracy and stability, effectively adapts to data security risk environments, and offers a feasible intelligent decision aid tool for data security management.