In recent years, the application and popularization of big data have become more and more extensive, and data has become an important asset and core competitiveness of enterprises and organizations. However, while the application of data generates value, the security and privacy protection of data are also becoming increasingly prominent. The leakage of sensitive data not only brings huge financial losses to enterprises and organizations, but also faces legal and regulatory penalties. How to do a good job in the security management of sensitive data and effectively ensure the safe use of sensitive data has become a key issue that government and enterprise organizations and relevant persons in charge of data security need to pay attention to and solve at present and even for a long time in the future.
At present, the main problems faced by enterprises and organizations in the management of sensitive data assets include unreasonable process formulation, imperfect permission management, manual desensitization, and lack of supervision of the use of sensitive data assets. These issues can lead to data breaches, misuse, tampering, or unauthorized access and use, resulting in significant security risks and losses for organizations. The specific embodiment is as follows:
1. The process formulation is unreasonable.
The value and usefulness of sensitive data is high, so more people are involved in cross-departmental and cross-organizational data use requests. Lack of effective management of sensitive data request and outgoing processes can significantly increase the risk of data breaches. Moreover, if the process itself is not reasonable, it will also reduce the efficiency of related work and affect the circulation and full utilization of data.
2. Imperfect authority management.
Access to and use of sensitive data requires strict permission management. However, in practice, due to imperfect permission management or overly broad permission allocation, unauthorized persons may access and use sensitive data, which in turn increases the risk of data breaches.
3. There are many problems with manual desensitization.
When actually performing data distribution operations, there is a lack of professional tool support, such as data masking products or data distribution and sharing platforms, which requires manual data processing. This can not only lead to the quality of the distributed data not meeting the requirements, but also consume a lot of time and labor costs.
4. After the distribution of data without supervision of asset use, two aspects of supervision should be carried out: one is to ensure that the distributed data is only used reasonably by authorized personnel in approved scenarios, and once the leakage occurs, it can be traced back to the complete distribution chain; The second is to make detailed records of the data distribution and sharing process for post-event analysis and tracking. This allows for better governance of the use of sensitive data.
In view of the main problems faced in the process of sensitive data asset management, Ankki Technology has been deeply involved in the data security industry for 15 years, and provides solutions and solutions for the security management of sensitive data assets based on historical project experience and best practices.
1. Find out the family background and sort out the distribution of sensitive data.
Enterprises and organizations must have a clear understanding of the distribution of their sensitive data, and strictly follow national laws and the norms, standards, and requirements of their industries to ensure the safe outgoing of data. In terms of data use and management, it is also very important to do a good job of data classification and classification. Therefore, whether it is data management or the open use of data, it is first necessary to find out the bottom line, grasp the distribution of sensitive data, and lay a good foundation for subsequent work. As a professional asset sorting software, the classification and grading system of Ankki Technology has built-in rich and professional classification and grading standards, which can achieve zero configuration and use. With the help of leading intelligent classification and grading algorithms and models, it helps users quickly and efficiently sort out assets. The sorted asset classification and grading list can be shared with other systems to further enhance the value of asset combing.
2. Data desensitization to avoid sensitive data leakage.
To prevent the leakage of sensitive data caused by direct use, it is usually necessary to desensitize sensitive data for use, outgoing, and sharing. For different scenarios, data masking methods can be divided into static data masking and dynamic data masking methods.
Static masking: automatic data masking.
In order to solve the problems in the traditional data masking process, professional static masking products are usually used to achieve automatic data masking services. In the traditional process, DBAs need to manually filter data from the database and write scripts for transformation, which is time-consuming and error-prone, and the quality of the transformed data may not meet security and availability requirements. By adopting a static desensitization mechanism, the traditional process can be changed to achieve automatic extraction, automatic deformation, and automatic distribution. This improves productivity and quality, and ensures that the masked data is highly secure and available to meet business needs.
Dynamic desensitizer: meets the needs of data use.
For real-time data retrieval and on-demand distribution and sharing scenarios, we can use the dynamic masking mechanism. By deploying a dynamic masking device between the distribution object and the original data, the data desensitization and distribution can be carried out in real time based on the identity of the data requester and the sensitive type and level of the data to be accessed. This allows for on-demand use and ensures data security and control.
3. Watermark traceability, data leakage traceability.
After data assets are classified and sorted out and data desensitized, the security of sensitive data under general business access and data outgoing can be guaranteed. However, in order to ensure the access to sensitive data and the legitimate use of outgoing data, the necessary watermark can be applied to sensitive data, that is, the identity information of the data owner or operator can be implanted into the data, so as to realize the confirmation of data rights and accurately trace the source and determine responsibility in the case of data leakage, so that those who leak sensitive data have nowhere to hide.
4. Process control, technology and management are jointly guaranteed.
Sensitive data process management aims to ensure that sensitive data is effectively managed and controlled throughout its lifecycle. This includes identifying sensitive data, classifying and grading data, establishing access rights and security policies, documenting data usage and distribution, and more. By establishing a sound management mechanism and standardized processes, the security and privacy of sensitive data can be protected to the greatest extent and prevent unauthorized access and abuse.
In the management of sensitive data processes, the confidentiality, integrity, and availability of data need to be considered, while ensuring compliance with relevant laws and regulations, and compliance requirements. Managers should work closely with technical staff to develop detailed management plans and operating procedures, and conduct regular risk assessments and internal audits to continuously improve and refine the management system for sensitive data.
In the era of data for everyone, data has become an important resource for serving users and a key factor for generating great value. As data continues to grow and its applications expand, the value of protecting sensitive data is becoming more and more important. Only through comprehensive, scientific and standardized management and technical support can we ensure the security, compliance and reliable use of sensitive data, and provide a solid guarantee for the sustainable development of the era of data for everyone.