With data being defined as the fifth major production factor, it has become the core driving force of digital economic development. The full utilization and open sharing of data resources bring convenience to government and enterprise units, but also bring corresponding data security risks. Therefore, understanding and dynamically mastering the situation of data assets, and continuously carrying out data asset mapping has become a top priority task for enterprises.
Only by defining, inventorying, and planning unordered proprietary data resources, and building a data asset management system with clear themes, perfect services, and clear rights and responsibilities, can we fully release the value of data elements in business.
Data is not equal to data assets; valuable data is data assets. In the process of practice, we find that with the development of user business, the data level increases geometrically every year, leading to more prominent complexity in the collection, interaction, sharing of business system data, and business interaction, making it more difficult to control the data security risks associated with it.
It should be noted that data exists in two states: static data in storage status, and dynamic data in transmission or use. The risk of data in a static state is relatively controllable, and the most direct method is data encryption. However, the value of data lies in circulation and sharing, so liquidity becomes the greatest risk facing data. Monitoring and tracing of流动数据的flowing data becomes a key link in data security.
The organization's macro control of all data assets is an essential measure for the sorting of static data assets. In addition to achieving the purpose of understanding the inventory, it is more important to assess the risks of these assets.
On the other hand, because the greatest risk of data comes from transmission and use, one of the important bases for assessing data risk is to monitor and analyze the overall data assets, which are frequently accessed and dynamic, and which are basically static and relatively safe. Since not all data is equally important, the classification and grading of data assets is the first step in risk assessment. How to formulate practical and effective data asset security protection measures and use strategies for general data, important data, and core data, to ensure the safe and compliant use of data assets, is the primary issue in the implementation of data security governance, and the data asset survey system is a preferred tool for enterprises to build data governance systems.
During the data asset survey process, the data asset survey system automatically scans or manually configures various data asset storage systems in the network, including databases, big data platforms, file servers, etc. The platform scans the data asset content at scheduled or on-demand times, establishes a data asset catalog based on data categories, levels, and the number of data assets, and draws a data asset distribution map based on application servers. Based on this information, the decision-making layer of the enterprise can formulate data security protection measures and data use strategies to ensure the efficient and compliant operation of data business.
During the process of selecting a data asset survey system, there are two aspects that need to be paid special attention to: A) whether the data survey results are accurate and timely, and B) whether it will introduce security risks beyond the normal safe use of data business.
Firstly, let's talk about the accuracy and timeliness of data survey results. If the accuracy is lost due to timely failure in formulating strategies, the strategies formulated based on this survey result may not achieve the expected effect.
Currently, enterprises all have massive amounts of data, often stored in multiple servers, and at the same time, data usage and updates are very frequent. The complete mapping of these static massive data (data set of mapping data) requires a relatively long time, usually before the mapping process is completed, the data set has changed. The data level of a data set depends on the highest level of its unit data. For example, in the static data mapping process, if there is no unit data in the data set that reaches the core data level, but the data unit that changes in this data set during the mapping process reaches the core data level, the mapping result will not define the data set as core data, but in fact, the level of the mapped data set has become core data.
Compared to the security protection measures and strategies for core data, enterprises often differentiate from other data levels. Therefore, the accuracy and timeliness of data mapping results are very important for the decision-making level of the enterprise. However, static data mapping results are often difficult to keep up with the actual data situation that changes at any time, without accurate classification and grading of modified data, and new data is not mapped. No matter how many times the static mapping process is repeated, it cannot guarantee the accuracy and real-time nature of the data set mapping results. Therefore, it is necessary to monitor the real-time updated data while mapping, classify and grade these data, and then integrate them with the results of static data asset mapping.
Thus, for the massive data asset mapping process, we only need to carry out a static mapping once, and then through dynamic mapping, monitor the differential parts of data changes (modification/deletion/addition) in real time, so as to ensure the real-time and accuracy of the data asset mapping system results.
However, real-time monitoring requires different technical means, such as the need to intercept network traffic in real time, decrypt encrypted traffic, extract data from network traffic, and then map the differential data, which is exactly what increases the technical value and complexity of the data asset mapping system.
Secondly, let's talk about the data security protection measures. As mentioned above, the accuracy and timeliness of data mapping results will provide the decision-making level of the enterprise with the basis for formulating data security measures and strategies. However, many traditional data asset mapping systems collect all the login IDs and passwords of data services to count and sort out the enterprise's data assets. To some extent, this does provide convenience for enterprises using traditional data asset mapping systems: A) remote access, and B) the ability to call the entire enterprise's data.
It is evident that this approach turns the data asset mapping system into a universal key to obtain all data assets of the enterprise, and the data asset mapping system will naturally become the main target of attacks by hackers and other illegal elements. The decision-making level of the enterprise not only needs to consider the security issues of data business but also needs to ensure the security of the data asset mapping system, and it must be a key protection. This is because the data asset mapping system contains all the data of the enterprise, and the security risk of the enterprise's data assets is greater than that of any data business.
To control and reduce the impact of asset survey systems on the security risks of enterprise data assets, the innovative generation of asset survey systems can install agents on data servers. Agents can only obtain data from the local machine and classify data, but only feedback the results to the asset survey system. In this way, the asset survey system does not need to carry all the original data of the enterprise, does not need to access data remotely, and will not increase the risk level of enterprise data assets. At the same time, if necessary, enterprise IT administrators can control the strategy of agents accessing the asset survey system through firewall policies, such as the IP address of the asset survey system, session time, session frequency, and other parameters, without frequent modification of the asset survey system and its agents. The advantage of doing so is that the introduction of the asset survey system will only help the enterprise decision-making level to formulate better strategies without introducing additional data security risks.
As a professional domestic data security technology innovation company, Holographic Network Defense Technology, based on many years of product development practice, has launched the holographic data asset survey system, which is a leading technical tool for comprehensive data asset governance. The product adopts the B/S structure and a big data underlying technology framework, carries advanced technology engines such as automatic discovery of data assets, intelligent scanning of data architecture, and automatic identification of sensitive assets, which can help enterprises quickly locate their internal network data services, and achieve the organization of data assets in the target environment, that is, complete a comprehensive survey and investigation of data assets, understand the types, distribution, permissions, distribution, flow, and use of data assets, classify and classify data assets by different categories and confidentiality levels, and build a data asset catalog to enable targeted protection of sensitive data, while visually presenting the status of data use, data flow analysis, and access behavior analysis, enabling customers to more clearly and intuitively grasp the security status and related information of sensitive data.
The holographic data asset survey system has the characteristics of wide support range, fast identification speed, high usability, and strong versatility. It helps enterprises quickly discover and organize their data asset conditions, assist enterprises in the construction of data classification and level, understand the flow of data assets and user permissions, and can also meet various regulatory and detection scenarios. It has replaced the traditional data asset management and organization mode, greatly improving the quality of data organization, and thus reducing the management costs of enterprises, safeguarding the construction of enterprise data security.
Data asset survey is just the first step in data security construction, and with the vigorous development of the digital economy, the demand for data security is constantly increasing. Holographic Network Defense will continue to invest in technical research on data security, enhance data security governance capabilities, expand data security empowerment capabilities, and better provide leading data security construction and governance solutions for customers in various industries.
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