Data asset classification protection method based on data evaluation pricing

Abstract

The invention belongs to the technical field of data asset management, and discloses a data asset classification protection method based on data assessment pricing, which comprises the following steps: S1, data collection: collecting data assets to be assessed, priced and subjected to classification protection, the data assets comprise business data, customer data and financial data in an enterprise, and market data and partner data which are acquired from the outside; and S2, data asset assessment: comprehensively assessing the characteristics, usage scenarios and potential risks of the data assets to generate a pricing model of the data assets. According to the scheme, value evaluation is performed on the data assets, the data classification is determined in combination with business and safety sensitivities of the data assets, and corresponding safety protection strategies are formulated for different types of data, so that the management efficiency of the data assets is improved, safety resources are reasonably allocated, the overall safety cost is reduced, and the precision and effectiveness of data protection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset management, and more specifically, to a data asset classification and protection method based on data evaluation pricing. Background Art

[0002] In the current information age, data assets have become an important strategic resource for enterprises. The security and effective management of data assets are crucial to the operation of enterprises. However, with the diversification of data types and usage scenarios, the management and protection of data assets face great challenges.

[0003] Traditional data protection methods often adopt a one-size-fits-all strategy, that is, they do not distinguish between the importance and sensitivity of data and implement the same level of protection measures for all data. This approach not only increases the operating costs of enterprises, but may also lead to the leakage or loss of important data due to improper protection. In addition, traditional methods fail to effectively consider the economic value of data, resulting in irrational allocation of protection resources and inability to effectively respond to the growing demand for data security.

[0004] Therefore, there is an urgent need for a method that can classify and protect data assets according to their economic value and risk level, so as to optimize resource allocation and improve the pertinence and efficiency of data protection. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a data asset classification protection method based on data evaluation pricing.

[0006] To solve the above problems, the present invention adopts the following technical solutions: A data asset classification protection method based on data evaluation pricing includes the following steps: S1. Data collection: Collect data assets to be evaluated, priced, and classified for protection. The data assets include business data, customer data, financial data within the enterprise, and market data and partner data obtained from outside. S2. Data asset assessment: Conduct a comprehensive assessment of the characteristics, usage scenarios, and potential risks of data assets to generate a pricing model for data assets; S3. Data asset classification: according to the data asset pricing results, data assets are classified into different categories, including data assets of high security level, medium security level and security level; S4. Development of protection strategies: Develop corresponding access control, encryption, storage and backup protection measures based on the characteristics and risk levels of different types of data.

[0007] As a further solution of the present invention: in the data asset evaluation step, The characteristics of the data assets include the size of the data, the structure of the data and the timeliness of the data; The usage scenarios include the frequency of data usage, the purpose of data usage, and the scope of data sharing; The potential risks include data leakage risk, data tampering risk, and data loss risk.

[0008] As a further solution of the present invention: in the data asset classification step, the high security level data assets are data with high value and high commercial and security sensitivity; The medium security level data assets are data with medium value and medium commercial and security sensitivity; The low security level data assets are data with low value and low commercial and security sensitivity; The boundaries of the interval are set based on the enterprise's data management strategy and risk tolerance.

[0009] As a further solution of the present invention: in the protection strategy formulation step, High-level encryption technology is used to store high-security data assets, and access rights are restricted to specific senior managers and core business personnel. Multiple authentication and encrypted tunneling technologies are used during data transmission. For medium-security-level data assets, conventional encryption technology is used for storage, access rights are granted to employees of relevant business departments, and basic encryption protocols are used for data transmission; For low-security-level data assets, simple storage encryption or no encryption is used, and access permissions are relatively loose, requiring only basic authentication for access.

[0010] As a further solution of the present invention: in the backup protection measure, Real-time backup and off-site multi-copy backup strategies are used for high-security data assets; A regular backup strategy is adopted for medium-security-level data assets, and the backup frequency is lower than that for high-security-level data assets; For data assets with low security levels, the strategy of irregular backup or backing up only the key parts is adopted based on storage space and cost-effectiveness considerations.

[0011] As a further solution of the present invention: the pricing model in the data asset evaluation step is constructed based on the following formula: ,in is the price of data assets, Factors related to the characteristics of data assets, For usage scenario related factors, For potential risk-related factors, is the pricing function.

[0012] As a further solution of the present invention: It includes data scale S, data structure T and data timeliness E, and C=g(S,T,E), where g is the characteristic function.

[0013] As a further solution of the present invention: It includes data usage frequency F, usage purpose A and sharing scope M, and U=h(F,A,M), where h is the usage scenario function.

[0014] As a further solution of the present invention: the R includes data leakage risk L, tampering risk D and loss risk N, and R=k(L, D, N), where k is the potential risk function.

[0015] Compared with the prior art, the advantages of the present invention are: This solution evaluates the value of data assets, combines their commercial and security sensitivities, determines the classification of data, and formulates corresponding security protection strategies for different categories of data to improve the management efficiency of data assets, reasonably allocate security resources, reduce overall security costs, and improve the accuracy and effectiveness of data protection. DETAILED DESCRIPTION

[0016] The following is a clear and complete description of the technical solutions in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0017] A data asset classification protection method based on data evaluation pricing includes the following steps: S1. Data Collection: Collect data assets to be evaluated, priced, and classified for protection. Data assets include business data, customer data, financial data within the enterprise, as well as market data and partner data obtained from external sources.

[0018] S2. Data asset assessment: Conduct a comprehensive assessment of the characteristics, usage scenarios and potential risks of data assets to generate a pricing model for data assets to clarify the value of different data assets. An accurate pricing model helps companies consider the value of data assets in business decisions and respond to risks in a more targeted manner.

[0019] The characteristics of data assets include, but are not limited to, the size of the data (the size of the data volume), the structure of the data (such as structured, semi-structured or unstructured), and the timeliness of the data (the validity period or update frequency of the data). Usage scenarios include, but are not limited to, the frequency of data use, the purpose of data use (whether it is used for core business decisions or general queries), and the scope of data sharing (whether it is shared internally or can be shared publicly). Potential risks include, but are not limited to, data leakage risks (such as the possibility of data being illegally obtained), data tampering risks (the possibility of data being maliciously modified), and data loss risks (the possibility of data loss due to hardware failure, software errors, etc.). Potential risks are determined by analyzing the security of the data storage environment, the security of the data transmission path, and the authority management status of data access.

[0020] The pricing model in the data asset valuation step is constructed based on the following formula: ,in, is the price of data assets, Factors related to the characteristics of data assets, For usage scenario related factors, For potential risk-related factors, is the pricing function.

[0021] Furthermore, C=g(S,T,E); U=h(F,A,M); R=k(L,D,N); where S is the data size, T is the data structure, and E is the data timeliness. F is the frequency of data use, A is the purpose of use, M is the sharing scope, L is the risk of data leakage, D is the risk of data tampering, N is the risk of data loss, g, h, and k are the characteristic function, usage scenario function, and potential risk function, respectively.

[0022] S3. Data asset classification: Based on the data asset pricing results, data assets are divided into different categories, including high security level, medium security level and security level data assets. This classification method makes data protection more targeted, and different levels of data can implement different levels of security measures, thereby making more effective use of protection resources.

[0023] Among them, high-security data assets are data with high value and high commercial and security sensitivity, such as the company's core business secrets, customer privacy information, etc. Medium-security data assets are data with medium value and medium commercial and security sensitivity, such as general business data. Low-security data assets are data with low value and low commercial and security sensitivity, such as some public corporate promotional materials. The boundaries of the intervals are set according to the company's data management strategy and risk tolerance.

[0024] S4. Protection strategy formulation: formulate corresponding access control, encryption, storage and backup protection measures according to the characteristics and risk levels of different types of data. This not only improves the security of data protection, but also optimizes the cost-effectiveness of data management.

[0025] For high-security data assets: high-level encryption technology is used for storage, access rights are restricted to specific senior managers and core business personnel, and multiple authentication and encryption tunnel technology are used during data transmission. The details are as follows: Access control: Use multi-factor authentication, such as passwords, fingerprint recognition, and dynamic verification codes, to limit access to specific senior managers and core business personnel with high-level permissions. This can greatly reduce the possibility of illegal access.

[0026] Encryption: Use strong encryption algorithms such as Advanced Encryption Standard (AES) for encryption. The AES algorithm has high security and can effectively protect the confidentiality of data.

[0027] Storage: Stored in a dedicated storage device or area with high security, such as a specific partition in an encrypted storage server or secure cloud storage. This storage method can prevent data from being destroyed by physical and cyber attacks.

[0028] Backup: Use real-time backup, off-site multiple-copy backup, or short-time interval backup, and store in off-site high-security backup facilities. This way, even if there is a problem with local data, the data can be quickly restored, and off-site backup can prevent local disasters (such as fire, flood, etc.) from damaging the data.

[0029] For data with medium security level, conventional encryption technology is used to store medium security level data assets, and access rights are granted to employees of relevant business departments. Basic encryption protocols are used for data transmission, and a regular backup strategy is adopted. The backup frequency is lower than that of high security level data assets.

[0030] For low-security data assets, simple storage encryption or no encryption is used, and access rights are relatively loose, requiring only basic authentication for access. Based on storage space and cost-effectiveness considerations, an irregular backup strategy or a strategy of backing up only key parts is adopted.

[0031] In summary, the method of the present invention can effectively improve the protection efficiency and security of data assets while reducing management costs, which is particularly beneficial for data-intensive enterprises.

[0032] The above is only a preferred specific implementation of the present invention; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and its improved conception within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A data asset classification protection method based on data evaluation pricing, characterized by: The following steps are involved: S1. Data collection: Collect data assets to be evaluated, priced, and classified for protection. The data assets include business data, customer data, financial data within the enterprise, and market data and partner data obtained from outside. S2. Data asset assessment: Conduct a comprehensive assessment of the characteristics, usage scenarios, and potential risks of data assets to generate a pricing model for data assets; S3. Data asset classification: according to the data asset pricing results, data assets are classified into different categories, including data assets of high security level, medium security level and security level; S4. Development of protection strategies: Develop corresponding access control, encryption, storage and backup protection measures based on the characteristics and risk levels of different types of data.

2. According to claim 1, a data asset classification protection method based on data evaluation pricing is characterized by: In the data asset evaluation step, The characteristics of the data assets include the size of the data, the structure of the data and the timeliness of the data; The usage scenarios include the frequency of data usage, the purpose of data usage, and the scope of data sharing; The potential risks include data leakage risk, data tampering risk, and data loss risk.

3. According to claim 1, a data asset classification protection method based on data evaluation pricing is characterized by: In the data asset classification step, the high security level data assets are data with high value and high commercial and security sensitivity; The medium security level data assets are data with medium value and medium commercial and security sensitivity; The low security level data assets are data with low value and low commercial and security sensitivity; The boundaries of the interval are set based on the enterprise's data management strategy and risk tolerance.

4. According to claim 1, a data asset classification protection method based on data evaluation pricing is characterized by: In the protection strategy formulation step, High-level encryption technology is used to store high-security data assets, and access rights are restricted to specific senior managers and core business personnel. Multiple authentication and encrypted tunneling technologies are used during data transmission. For medium-security-level data assets, conventional encryption technology is used for storage, access rights are granted to employees of relevant business departments, and basic encryption protocols are used for data transmission; For low-security-level data assets, simple storage encryption or no encryption is used, and access permissions are relatively loose, requiring only basic authentication for access.

5. According to claim 1, a data asset classification protection method based on data evaluation pricing is characterized by: In the backup protection measures, Real-time backup and off-site multi-copy backup strategies are used for high-security data assets; A regular backup strategy is adopted for medium-security-level data assets, and the backup frequency is lower than that for high-security-level data assets; For data assets with low security levels, the strategy of irregular backup or backing up only the key parts is adopted based on storage space and cost-effectiveness considerations.

6. According to claim 2, a data asset classification protection method based on data evaluation pricing is characterized by: The pricing model in the data asset evaluation step is constructed based on the following formula: ,in is the price of data assets, Factors related to the characteristics of data assets, For usage scenario related factors, For potential risk-related factors, is the pricing function.

7. A data asset classification protection method based on data evaluation pricing according to claim 6, characterized in that: Said It includes data scale S, data structure T and data timeliness E, and C=g(S,T,E), where g is the characteristic function.

8. The data asset classification protection method based on data evaluation pricing according to claim 6 is characterized by: Said It includes data usage frequency F, usage purpose A and sharing scope M, and U=h(F,A,M), where h is the usage scenario function.

9. The data asset classification protection method based on data evaluation pricing according to claim 6 is characterized by: The R includes data leakage risk L, tampering risk D and loss risk N, and R=k(L, D, N), where k is the potential risk function.