Data Security Risk Assessment Method Based on Big Data Technology

With the acceleration of digital transformation, the risk of data leakage and destruction is becoming increasingly serious, and traditional data security assessment methods may not be able to effectively respond to complex and ever-changing security threats. In order to break through the bottleneck, this article conducts a comprehensive analysis of network data flow, host request flow, and business response flow during the information model construction stage. The article evaluates the security status of network traffic by setting a time window, recording and extracting network flow data features, and using a classification module. Subsequently, for the service request flow and response flow, their behavioral characteristics are extracted and a classification module is applied to detect abnormal behavior. Secondly, the article measures the security risks of information systems, using the VAR model to evaluate information security risks. The risk assessment process is simplified by calculating the probability of asset value loss, and the evaluation indicators are determined by combining weights. Finally, an information security situational awareness optimization model for decision tree calculation is constructed, and a perception matrix is established to quantify static and dynamic sensing indicators. The research results indicate that the risk assessment model based on decision tree algorithm can effectively identify potential security risks with an accuracy of 92.50%, and its assessment results have a high consistency with actual security events.