## 1 Introduction
Elastic scalability of resources: As data processing by the application side requires computational resources, which fluctuate with varying data volumes, the deployment should allow for elastic scalability of resources to minimize investment costs for the application side. 2. Low bandwidth utilization cost and reduced data processing latency: The data traffic accessed by the application side needs to pass through the regulatory outpost. It is crucial to ensure low bandwidth utilization costs and reduced data processing latency to minimize any impact on the application side's user experience. 3. Ensuring data compliance: In the context of open banking, the application side tends to locally store open banking data, necessitating compliance checks on these data to prevent potential leaks. As shown in Figure 2, a way is given for the application side to perform operations such as data desensitization and watermarking locally to enhance data security, in which data classification and grading is the basis. Hence, the automated classification and grading of sensitive data in the financial sector are garnering increasing attention. Firstly, financial institutions should conduct a comprehensive assessment of their own data landscape to achieve a visualized map of sensitive data assets before engaging in data sharing. Secondly, for third-party service providers collaborating with financial institutions, it is imperative that they enhance their data security management capabilities in accordance with government regulatory requirements and contractual agreements with financial institutions, which include encrypting sensitive data during storage or implementing data anonymization techniques, with the prerequisite being the prompt identification and classification of sensitive data transmitted during the collaboration process. From the aforementioned scenario, the automated classification and categorization of sensitive data in the financial domain is a fundamental capability of the technology platform. Currently, the financial industry employs two primary methods for data classification and grading. One involves manual classification, which spans multiple departments, leading to a lengthy and inefficient process, and it lacks reusability, posing limitations on its scalability and adaptability. Another relies on automated classification and grading based on pattern matching, utilizing internally constructed data dictionaries. However, this approach suffers from low accuracy rates, especially when dealing with incomplete data dictionaries. Figure 2: Data processing workflow in regulatory outposts within edge cloud scenarios. We conducted a comprehensive study on various security issues within open banking and proposed a data security framework. In this paper, our primary focus is on the issue of classifying sensitive data. Security measures such as watermarking are addressed in other works [7]. Building upon the aforementioned challenges, we present a data classification and grading framework to the financial industry which adheres to the relevant industry standards. Our framework encompasses both structured and unstructured data classification and grading. For structured data, we introduce a novel sensitive data classification and grading algorithm named UP-SDCG, leveraging self-enrichment and broadening techniques. Additionally, we enhance the financial data hierarchical classification by employing an augmentation model to expand keywords and lexicons, which significantly boosts the accuracy and recall of data classification and grading. Furthermore, we incorporate a synonym discrimination model to further expand the keywords and dictionaries in the industry data hierarchical classification library, resulting in improved accuracy and recall of data classification and grading. In our future work, we aim to further develop a scheme for classifying and grading unstructured sensitive data. This scheme will also support the coarse-grained classification of document data containing sensitive information.