# Automated Identification of Sensitive Financial Data Based on the Topic Analysis
## 1 Introduction  
Data governance [1] encompasses a range of activities and measures aimed at managing, safeguarding, and optimizing data throughout its entire life cycle. The data's planning, collection, storage, processing, analysis, sharing, and destruction are all included to ensure their quality, reliability, security, and availability. Data governance enables users to better understand, manage, and use data assets. A set of frameworks and methods are provided to ensure data compliance, consistency, integrity, discoverability, traceability, and credibility. At present, data governance is facing a series of important issues [2] in the financial industry and other industries too. These issues include the security risks, privacy leakage, management, and access control of data, which may lead to data leakage and abuse. The security classification of data has become an important task in the face of data governance. Current research focuses on security classification and data categorization. Existing methods mainly pay attention to identifying sensitive information in modules, such as the user interface and user input. The user interface proposed by [PERSON] et al. [3] in conjunction with natural language processing technology enables automatic checking from the static analysis. The interface is used to identify sensitive user input methods that contain key user data (e.g., user credentials and financial and medical data). [PERSON] et al. [4] detected semanticinformation within application layout resources and program code to subsequently analyze potential locations for safety-critical data. Diverse security vulnerabilities in mobile applications are analyzed. The aforementioned research detects and safeguards the user's sensitive input by analyzing the sensitivity of data values. However, it is inefficient to repeatedly identify and detect different data values of the same data field. For example, if a user interface contains a data field \"user ID number\", both the data field and its corresponding value are considered sensitive. Identifying sensitive data from the descriptive text of data fields has become a method of improvement. [PERSON] et al. [5] comprehensively considered the formation of semantic, grammatical, and lexical information. Sensitive data are identified through the semantics of their descriptive text. A conceptual space is introduced to represent the concept of privacy, which supports users' flexible needs in defining sensitive data. The convolution-based method proposed by [PERSON] et al. [6] improves the traditional features of the hierarchical method through activation functions, with an effective mode of learning introduced in the process. The nonlinear characteristics in data are utilized through the optimization of logical regression learning. The above method has achieved good results when judging the sensitivity of text with relatively complete semantics. However, the ambiguity caused by descriptive texts with shorter field lengths and less semantic information still has a greater impact on classification, which is extremely common in real business scenarios. The main direction of this research is presented below. The sensitivity discrimination of the field is conducted when there is limited semantic information available. The realization of multi-sensitivity classification should meet the requirements of specific business scenarios. This classification is not just a simple classification of sensitive and nonsensistive levels. The contributions of this paper are summarized as follows. 1. A practical and applicable method has been proposed to address the challenge of classifying text with weak semantic information at a sensitive level. This advancement significantly enhances the feasibility of implementing sensitive data classification and grading, laying a solid foundation for ensuring data security protection. 2. The limitations on the extensibility of the model across different fields have been eliminated.