Hierarchical-Based Dynamic Scenario-Adaptive Risk Assessment for Power Data Lifecycle

In an era marked by rapid advancements in information technology, the task of risk assessment for data security within the complex infrastructure of the power grid has become increasingly vital. This paper introduces a novel methodology for dynamic, scenario-adaptive risk assessment, specifically designed to address the entire lifecycle of power data. Integrating hierarchical analysis with fuzzy comprehensive evaluation, our approach provides a flexible and robust framework for assessing and managing risks in various scenarios. This method enables the generation of adaptive weight matrices and precise risk level determinations, ensuring a detailed and responsive analysis of data security at each lifecycle stage. In our study, we applied predictive analytics and anomaly detection to conduct a thorough examination of diverse data scenarios within the power grid, aiming to proactively identify and mitigate potential security threats. The results of this research demonstrate a significant enhancement in the effectiveness of risk detection and management, leading to improved data protection and operational efficiency. This study contributes a scalable, adaptable model for data security risk assessment, meeting the challenges of big data and complex information systems in the power sector.