As a well-known Internet bank in China, a bank has always adhered to the concept of technology-enabled finance with the overall strategic goal of building a "smart interconnected bank". Through the exploration and integration of digital technologies such as AI, big data, and cloud computing with financial services, we can provide customers with convenient, efficient, and high-quality Internet financial services at a lower cost.In just a few years since its establishment, the bank has completed the upgrade of its enterprise-level IT architecture, achieved remarkable technological changes, and provided solid technical support for addressing the issues of efficiency, cost, stability and security in the bank's digital business. In the process of IT architecture upgrading, the contradiction between R&D efficiency and R&D security has become one of the key issues affecting the development of digital financial business, and the original tools and solutions cannot effectively balance this contradictionTraditional testing is incomplete, and there are many hidden dangers. The original security tests are usually based on known attack patterns and vulnerability libraries, and many test methods only focus on a single security layer, such as the network layer and the application layer, while ignoring the interaction and dependencies between different layers, resulting in the omission of security issues and laying hidden dangers for the bank's digital financial business. **Low detection efficiency slows down the R&D progress. Traditional static** detection is not only inefficient, but also has a high false alarm rate. A large number of false positives will waste developers' time and energy to investigate, seriously affect the delivery speed of the project, and cannot meet the bank's efficiency requirements for the R&D system. Data behavior is not transparent, and risk points are difficult to locate. During the original testing process, the data flow and behavior within the DUT system were not transparent, and the comprehensive monitoring and analysis of data behavior was lacking, and developers could not accurately determine which data was sensitive and which operations were dangerous, so they could not fix such problems in time, increasing the risk of data breaches, and potentially leading to serious data privacy compliance issues. In the process of upgrading and renovating the R&D system, the bank deeply realized the limitations of the original security testing tools, and decided to use the open-source network security gray box security testing tool Vulhunter to integrate into the R&D process, realizing the comprehensiveness and efficiency of security testing. Comprehensive coverage and in-depth detection:VulHunter uses an interactive detection approach that analyzes the behavior of applications and detects potential security threats while the software under test is running. This method combines the advantages of dynamic and static testing to effectively improve the security test coverage, and can deeply detect logic defects and abnormal behaviors that are difficult to find. Precise positioning and timely and efficient:Through the close integration with the R&D process, VulHunter can detect and identify security vulnerabilities during submission, build, or test execution, and accurately locate specific security vulnerabilities to ensure that security issues can be dealt with at an early stage, thereby reducing the cost of remediation and greatly improving R&D efficiency. Automated vs. Continuous Inspection:The automation features of VulHunter can be seamlessly integrated into the bank's R&D process, including the continuous integration and continuous deployment (CI CD) pipeline, which ensures that security testing becomes a necessary part of "no manual intervention", realizes continuous security detection and automatically generates inspection reports, and greatly improves the overall project cycle.
Vulhunter, an open-source cybersecurity gray-box security testing platform, provides the Internet bank with in-depth software security detection capabilities, helping the bank identify and solve potential security problems in advance during the R&D stage, thereby reducing the security risks caused by vulnerabilities. In addition, the automated continuous detection capability provided by VulHunter ensures that the bank's software system is always efficient and safe in the process of continuous iteration and update, realizes the "double effect improvement" of R&D efficiency and security for the bank, and ensures the high-quality development of the bank's financial business.
In the future, Open Source Network Security will continue to develop security solutions in the digital scenarios of the financial industry, and strive to improve the security capabilities of the R&D system through technology innovation and application, so as to provide customers in the financial industry with more comprehensive and efficient security guarantees. Help customers in the financial industry build a solid and reliable security line of defense, ensure the security and stability of financial business, and promote the innovation and development of financial technology. 
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A private bank in southwest China: R&D to improve quality and efficiency, and build a leading digital private bank.