A data governance system and method based on knowledge management to identify data security policies

Abstract

The present invention proposes a data governance system and method based on knowledge management to identify data security policies, which relates to the field of data security technology; it includes a data governance subsystem for collecting metadata; a data security knowledge base for storing data security policies and providing data security policy recommendation results to an implementation workbench; an implementation workbench for interacting with the data governance subsystem and the data installation knowledge base respectively; it is used to observe and mark metadata that requires adding data security policies, obtain data security policy recommendation results from the data security knowledge base, and further generate updated data security policies, and the updated data security policies are synchronized to the data governance subsystem and the data security knowledge base respectively; wherein, the data governance subsystem also executes the updated data security policies.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and in particular to a data governance system and method based on knowledge management to identify data security policies. Background Art

[0002] Data security management within a data governance system encompasses managing data security and preventing data leakage, corruption, tampering, and unauthorized access. Data security management implements data protection measures through specific data security policies. These policies primarily include data desensitization, data classification, and data manipulation, ensuring the protection of sensitive and confidential data.

[0003] In existing data governance systems, data security management first involves identifying the data security policy for each table field, and then implementing corresponding data protection measures based on these policies. This requires identifying the meaning of each table field and determining and implementing the corresponding data protection policy. This process is very time-consuming, and for similar data security management needs, a large amount of data governance experience cannot be inherited and reused, resulting in great waste.

[0004] Therefore, based on the above situation, it is very necessary to provide a data governance system and method based on knowledge management to identify data security policies, establish a security knowledge base in advance, summarize and organize security policy knowledge and policies, perform targeted processing on classified data, and improve the execution efficiency of security policies. Summary of the Invention

[0005] In view of this, the present invention proposes a data governance system and method based on knowledge management to identify data security policies for secure processing of specific data and prevent data leakage, tampering or destruction.

[0006] In one aspect, the present invention provides a data governance system for identifying data security policies based on knowledge management, comprising:

[0007] Data governance subsystem, used to collect metadata;

[0008] Data security knowledge base, used to store data security policies and provide data security policy recommendations to the implementation workbench;

[0009] The implementation workbench interacts with the data governance subsystem and the data security knowledge base. It is used to observe and mark metadata that requires data security policy additions, obtain data security policy recommendations from the data security knowledge base, and further generate updated data security policies. The updated data security policies are synchronized to the data governance subsystem and the data security knowledge base.

[0010] Among them, the data governance subsystem also implements updated data security policies.

[0011] Based on the above technical solution, preferably, the metadata collected by the data governance subsystem includes the first source data of the external system and the second source data of the data warehouse of the data governance subsystem; wherein, the first source data includes supplier, system, version, data source type, IP, port, user and password fields; the second source data includes schema name, table English name, table comment, field English name, field comment, data type, whether it is required and whether it is a primary key field.

[0012] Preferably, a workbench is implemented to observe and mark the metadata to which data security policies need to be added. This is to observe the metadata collected by all data governance subsystems and mark the obtained supplier, system, version, user name, password, table English name, table comment and field English name fields; the marked fields are the fields of the recommended results of the data security policy to be added.

[0013] Further preferably, the data security knowledge base presets mutually related supplier system tables, security protection data tables and data security policy relationship tables; wherein, the supplier system table includes, in sequence, a first system number column, a supplier column, a system number column and a version number column; the security protection data table includes a second system number column, a data warehouse layering column, a first data table coding column, a database model column, a table English name column and a table Chinese name column; the data security policy relationship table includes a third system number column, a second data table coding column, a field English name column, a field Chinese name column and a data security policy coding column; the contents of the marked fields of the implementation workbench are compared with the contents of each row of the supplier system table, security protection data table and data security policy relationship table preset in the data security knowledge base, and the data security policy recommendation results are obtained from the data security knowledge base; the contents of the first system number column, the second system number column and the third system number column are only used as serial numbers; the contents of the first data table coding column and the second data table coding column correspond to each other.

[0014] Further preferably, obtaining the data security policy recommendation result from the data security knowledge base is performed by querying the marked supplier field:

[0015] 1) If the vendor field, system number field, and version field of the metadata all exist in the vendor system table of the data security knowledge base, then the data security policy relationship table is further queried for the contents of the corresponding first data table code column, second data table code column, and data security policy code column. If the contents of the first data table code column and the second data table code column corresponding to the vendor field are identical, and valid data exists in the data security policy code column, then the data corresponding to the data security policy code is selected as the data security policy recommendation result;

[0016] 2) If the supplier field, system number field, and version field of the metadata all exist in the supplier system table of the data security knowledge base, then further query the contents of the corresponding first data table code column, second data table code column, and data security policy code column in the data security policy relationship table. If the contents of the first data table code column and the second data table code column corresponding to the supplier field are the same, but the data security policy code column does not have valid data, or the contents of the first data table code column and the second data table code column corresponding to the supplier field are different, then select the data corresponding to the lowest-level data security policy code as the data security policy recommendation result; and fill the currently generated lowest-level data security policy code into the data security policy code column of the data security policy relationship table;

[0017] 3) If the supplier field, system number field and version field of the metadata do not exist in the supplier system table of the data security knowledge base, the data corresponding to the lowest level data security policy code is automatically selected or manually selected as the data security policy recommendation result; and the currently generated lowest level data security policy code is filled in the data security policy code column of the data security policy relationship table.

[0018] More preferably, the data security policy recommendation results are arranged in ascending order of level, including data classification strategy, data operation strategy and desensitization strategy;

[0019] The data classification strategy is to classify metadata into level 1, level 2 and level 3 according to the importance of metadata.

[0020] Desensitization strategies are to change sensitive information in metadata through masking, hashing, truncation, or encryption operations;

[0021] Data operation strategies include querying metadata and publishing metadata.

[0022] Further preferably, the published metadata in the data operation strategy is to further divide the metadata into three categories: unconditional sharing, conditional sharing and non-sharing after executing the data classification strategy, and set a desensitizing algorithm for the conditionally shared metadata above level 2.

[0023] More preferably, the further generation of the updated data security policy is performed by the implementation workbench adding or deleting the contents of the data warehouse hierarchical columns of the security protection data table in combination with the system fields of the metadata after obtaining the data security policy recommendation results.

[0024] Further preferably, the updated data security policy is synchronized to the data governance subsystem and the data security knowledge base respectively. On the one hand, after the updated data security policy is generated, the data security policy knowledge corresponding to the updated data security policy is saved and merged into the preset supplier system table, security protection data table and data security policy relationship table of the data security knowledge base. The data security policy knowledge includes the supplier, system number, version number, data warehouse layer, data table code, database model, table English name, table Chinese name, field English name, field Chinese name and data security policy code field set in sequence. Each field of the data security policy knowledge corresponds one-to-one to the non-system number column of the supplier system table, security protection data table and data security policy relationship table.

[0025] On the other hand, the present invention also provides a data governance method for identifying data security policies based on knowledge management, which is configured with the above-mentioned data governance system for identifying data security policies based on knowledge management. The method includes the following steps:

[0026] S1: Collect metadata: Call the data governance subsystem to collect metadata;

[0027] S2: Implement the workbench to observe the metadata obtained by the data governance subsystem and identify the metadata that needs to be added with data security policies;

[0028] S3: The implementation workbench obtains data security policy recommendation results from the data security knowledge base;

[0029] S4: Adjust and supplement the data security policy to obtain an updated data security policy;

[0030] S5: Save the updated data security policy to the data security knowledge base;

[0031] S6: Synchronize the updated data security policy to the data governance subsystem;

[0032] S7: The data governance subsystem implements the updated data security policy.

[0033] The data governance system and method provided by the present invention, which is based on knowledge management and identification of data security policies, has the following beneficial effects compared with the existing technology:

[0034] (1) The present invention establishes a data security knowledge base, pre-configured with interrelated supplier system tables, security protection data tables, and data security policy relationship tables, covering all fields in the metadata that require recommended data security policies, facilitating identification by the implementation workbench and further processing by the data security knowledge base;

[0035] (2) In the process of obtaining data security policies, the present invention implements collaboration between the workbench and the data security knowledge base to generate data security policy knowledge corresponding to the updated data security policies. The data security policy knowledge is stored in the data security knowledge base so that it can be shared and reused when security policies are subsequently applied to metadata of the same type, thereby improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is a system architecture diagram of a data governance system and method based on knowledge management to identify data security policies in the present invention;

[0038] Figure 2 This is an overall flow chart of a data governance system and method based on knowledge management to identify data security policies in the present invention;

[0039] Figure 3 This is a flow chart of a data governance system and method for executing a desensitization strategy based on knowledge management to identify data security strategies according to the present invention;

[0040] Figure 4 This is a flow chart of the data operation strategy of a data governance system and method based on knowledge management to identify data security strategies in the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, on the one hand, the present invention provides a data governance system for identifying data security policies based on knowledge management, comprising:

[0043] Data governance subsystem, used to collect metadata;

[0044] Data security knowledge base, used to store data security policies and provide data security policy recommendations to the implementation workbench;

[0045] The implementation workbench interacts with the data governance subsystem and the data security knowledge base. It is used to observe and mark metadata that requires data security policy additions, obtain data security policy recommendations from the data security knowledge base, and further generate updated data security policies. The updated data security policies are synchronized to the data governance subsystem and the data security knowledge base.

[0046] Among them, the data governance subsystem also implements the updated data security policy, that is, realizes the collection, processing, use and supervision of metadata.

[0047] Depend on Figure 1 As can be seen, the implementation workbench and data governance subsystem are independently deployed for each data governance project. The implementation workbench provides independent tenants for each project, isolating the implementation work. The data security knowledge base is centrally deployed, providing SaaS services for each project. The data security knowledge base is open to all data governance projects, providing knowledge sharing for project implementation.

[0048] The metadata collected by the data governance subsystem includes first-source data from external systems and second-source data from the data governance subsystem's data warehouse. First-source data includes fields such as vendor, system, version, data source type, IP address, port, user, and password. Second-source data includes schema name, table English name, table comment, field English name, field comment, data type, required field status, and primary key field status.

[0049] The implementation workbench observes and marks the metadata that needs to add data security policies. This is to observe the metadata collected by all data governance subsystems and mark the obtained vendor, system, version, user name, password, table English name, table comment and field English name fields; the marked fields are the fields for the recommended results of the data security policy to be added.

[0050] The data security knowledge base presets interrelated supplier system tables, security protection data tables and data security policy relationship tables; among them, the supplier system table includes the first system number column, supplier column, system number column and version number column in sequence; the security protection data table includes the second system number column, data warehouse layer column, first data table code column, database mode column, table English name column and table Chinese name column; the data security policy relationship table includes the third system number column, second data table code column, field English name column, field Chinese name column and data security policy code column; the content of the marked fields of the implementation workbench is compared with the content of each row of the supplier system table, security protection data table and data security policy relationship table preset in the data security knowledge base, and the data security policy recommendation results are obtained from the data security knowledge base; the contents of the first system number column, the second system number column and the third system number column are only used as serial numbers; the contents of the first data table code column and the second data table code column correspond to each other.

[0051] The following table shows an example of a supplier system table.

[0052] SYSID PROVIDER_CODE SYSTEM_CODE VERSION 100001 Yunqi Datamiddleplatform V1.0.0 100002 Yunqi Smartform V2.0.0

[0053] SYSID in the supplier system table is the first system number column; PROVIDER_CODE is the supplier column; SYSTEM_CODE is the system number column; VERSION is the version number column.

[0054] The following table shows an example of a security protection data table.

[0055] SYSID DATAWAREHOUSE TABLE_ID SCHEMA TABLENAME_EN TABLENAME_CHN 100001 ODS 800001 Iw Staffinfo Faculty and Staff Information Form 100001 ODS 800002 Iw Userinfo User Information Table 100001 ODS 800003 Iw Schoolinfo School Information Sheet 100001 DWD 900001 Iw DWD_STAFFINFO Faculty and Staff Information Form

[0056] SYSID in the security protection data table is the second system number column; DATAWAREHOUSE is the data warehouse hierarchical column, ODS represents the source layer, and DWD represents the standard layer; TABLE_ID is the first data table code column; SCHEMA is the database mode column; TABLENAME_EN is the table English name column; TABLENAME_CHN is the table Chinese name column.

[0057] The following table shows an example of a data security policy relationship table.

[0058] SYSID TABLE_ID COLUMN_EN COLUMN_CHN POLICY_ID 100001 800001 XM Name 4000101 100001 800001 DHMH telephone number 4000102 100001 800001 SFZJH ID number 4000103

[0059] SYSID in the data security policy relationship table is the third system number column; TABLE_ID is the second data table code column; COLUMN_EN is the field English name column; COLUMN_CHN is the field Chinese name column; POLICY_ID is the data security policy code column. Different codes in this column correspond to different data security policies.

[0060] like Figure 3 and Figure 4 As shown in the figure, the data security policy recommendation results are arranged in ascending order of level, including data classification strategy, data operation strategy and desensitization strategy;

[0061] The data classification strategy categorizes metadata into Level 1, Level 2, and Level 3 based on increasing importance. Examples of these classifications are as follows: Level 1 data: If data is leaked, tampered with, damaged, or illegally acquired, used, or shared, it will cause moderate or no harm to the rights and interests of individuals or organizations. Level 2 data: If data is leaked, tampered with, damaged, or illegally acquired, used, or shared, it will cause severe harm to the rights and interests of individuals or organizations. Level 3 data: If data is leaked, tampered with, damaged, or illegally acquired, used, or shared, it will cause particularly severe harm to the rights and interests of individuals or organizations, or moderate harm to economic operations, social order, or the public interest.

[0062] Desensitization strategies modify sensitive information in metadata through masking, hashing, truncation, or encryption. Masking operations include date type masking, phone number masking, MAC address masking, IPv4 masking, IPv6 masking, IMEI mobile device identification masking, email masking, bank card number masking, ID card number masking, retain first n and last m, mask first n and last m, retain from x to y, mask from x to y, and name masking. These operations replace a portion of sensitive data with a mask, thereby hiding some of the sensitive data. Hashing operations, including those using algorithms such as MD5, SHA-1, SHA-256, and HMAC, can replace the content of sensitive data with a fixed-length hash value, facilitating storage while reducing the risk of sensitive data leakage. Truncation operations include date type truncation, vertical type truncation, and character shifting, which can truncate part or all of the sensitive data, limiting access to the sensitive data. Encryption operations include DES or AES encryption, protecting data confidentiality.

[0063] Data operation strategies include query metadata and release metadata. The release metadata section of the data operation strategy further categorizes metadata into three categories: unconditionally shared, conditionally shared, and not shared, after implementing the data classification strategy. A desensitization algorithm is implemented for conditionally shared metadata above level 2.

[0064] Get data security policy recommendations from the data security knowledge base by querying the marked supplier fields:

[0065] 1) If the vendor field, system number field, and version field of the metadata all exist in the vendor system table of the data security knowledge base, then the data security policy relationship table is further queried for the contents of the corresponding first data table code column, second data table code column, and data security policy code column. If the contents of the first data table code column and the second data table code column corresponding to the vendor field are identical, and valid data exists in the data security policy code column, then the data corresponding to the data security policy code is selected as the data security policy recommendation result;

[0066] 2) If the supplier field, system number field, and version field of the metadata all exist in the supplier system table of the data security knowledge base, then further query the contents of the corresponding first data table code column, second data table code column, and data security policy code column in the data security policy relationship table. If the contents of the first data table code column and the second data table code column corresponding to the supplier field are the same, but the data security policy code column does not have valid data, or the contents of the first data table code column and the second data table code column corresponding to the supplier field are different, then select the data corresponding to the lowest-level data security policy code as the data security policy recommendation result; and fill the currently generated lowest-level data security policy code into the data security policy code column of the data security policy relationship table;

[0067] 3) If the vendor field, system number field, and version field of the metadata do not exist in the vendor system table of the data security knowledge base, the data corresponding to the lowest-level data security policy code is automatically or manually selected as the data security policy recommendation result; and the currently generated lowest-level data security policy code is entered into the data security policy code column of the data security policy relationship table. If there is a corresponding data security policy recommendation result in the data security knowledge base, the data corresponding to the data security policy code is selected; if there is a corresponding data security policy recommendation result in the data security knowledge base, the system automatically generates it or manually selects and enters it, which can also form the corresponding data security policy recommendation result.

[0068] After the implementation workbench obtains the data security policy recommendation results, it further generates an updated data security policy. This is done by combining the metadata's system fields with the implementation workbench's metadata to add or delete the contents of the data warehouse hierarchical columns in the security protection data table. The addition operation here is to store the field contents corresponding to the data security policy recommendation results in the security protection data table. If there are no identical historical records in the security protection data table, the current data security policy recommendation results are added to the contents of the data warehouse hierarchical columns. If it is found that there are identical historical records in the security protection data table that are identical to the data security policy recommendation results, a deletion operation is performed to delete the identical historical records and save the current data security policy recommendation results to the contents of the data warehouse hierarchical columns.

[0069] Reference Figure 1 On the one hand, the updated data security policy is synchronized to the data governance subsystem and the data security knowledge base respectively. After the updated data security policy is generated, the data security policy knowledge corresponding to the updated data security policy is saved and merged into the preset supplier system table, security protection data table and data security policy relationship table of the data security knowledge base. The data security policy knowledge includes the supplier, system number, version number, data warehouse layer, data table encoding, database model, table English name, table Chinese name, field English name, field Chinese name and data security policy encoding field set in sequence. Each field of the data security policy knowledge corresponds to the non-system number column of the supplier system table, security protection data table and data security policy relationship table. The obtained data security policy knowledge generates corresponding records in the supplier system table, security protection data table and data security policy relationship table as a reference for subsequent data governance of the same type of project, thereby realizing knowledge reuse.

[0070] On the other hand, the present invention also provides a data governance method for identifying data security policies based on knowledge management, which is configured with the above-mentioned data governance system for identifying data security policies based on knowledge management. The method includes the following steps:

[0071] S1: Collect metadata: Call the data governance subsystem to collect metadata;

[0072] S2: Implement the workbench to observe the metadata obtained by the data governance subsystem and identify the metadata that needs to be added with data security policies;

[0073] S3: The implementation workbench obtains data security policy recommendation results from the data security knowledge base;

[0074] S4: Adjust and supplement the data security policy to obtain an updated data security policy;

[0075] S5: Save the updated data security policy to the data security knowledge base;

[0076] S6: Synchronize the updated data security policy to the data governance subsystem;

[0077] S7: The data governance subsystem implements the updated data security policy.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data governance system based on knowledge management to identify data security policies, characterized by: include: Data governance subsystem, used to collect metadata; Data security knowledge base, used to store data security policies and provide data security policy recommendations to the implementation workbench; Implement a workbench to interact with the data governance subsystem and data security knowledge base respectively; It is used to observe and mark metadata that requires adding data security policies, obtain data security policy recommendations from the data security knowledge base, and further generate updated data security policies. The updated data security policies are synchronized to the data governance subsystem and the data security knowledge base respectively; Among them, the data governance subsystem also implements the updated data security policy; The implementation workbench observes and marks metadata that require data security policy additions. This involves observing metadata collected by all data governance subsystems and marking the acquired vendor, system, version, user name, password, table English name, table comment, and field English name fields. The marked fields are the fields for which data security policy recommendations will be added. The data security knowledge base presets mutually related supplier system tables, security protection data tables, and data security policy relationship tables; wherein the supplier system table sequentially includes a first system number column, a supplier column, a system number column, and a version number column; the security protection data table includes a second system number column, a data warehouse layer column, a first data table code column, a database schema column, a table English name column, and a table Chinese name column; the data security policy relationship table includes a third system number column, a second data table code column, a field English name column, a field Chinese name column, and a data security policy code column; the contents of the marked fields of the implementation workbench are compared with the contents of each row of the supplier system table, security protection data table, and data security policy relationship table preset in the data security knowledge base, and data security policy recommendation results are obtained from the data security knowledge base; the contents of the first system number column, the second system number column, and the third system number column are only used as sequence numbers; the contents of the first data table code column and the second data table code column are the same; To obtain data security policy recommendations from the data security knowledge base, query the marked supplier field: 1) If the vendor field, system number field, and version field of the metadata all exist in the vendor system table of the data security knowledge base, then the data security policy relationship table is further queried for the contents of the corresponding first data table code column, second data table code column, and data security policy code column. If the contents of the first data table code column and the second data table code column corresponding to the vendor field are identical, and valid data exists in the data security policy code column, then the data corresponding to the data security policy code is selected as the data security policy recommendation result; 2) If the supplier field, system number field, and version field of the metadata all exist in the supplier system table of the data security knowledge base, then further query the contents of the corresponding first data table code column, second data table code column, and data security policy code column in the data security policy relationship table. If the contents of the first data table code column and the second data table code column corresponding to the supplier field are the same, but the data security policy code column does not have valid data, or the contents of the first data table code column and the second data table code column corresponding to the supplier field are different, then select the data corresponding to the lowest-level data security policy code as the data security policy recommendation result; and fill the currently generated lowest-level data security policy code into the data security policy code column of the data security policy relationship table; 3) If the vendor field, system number field, and version field of the metadata do not exist in the vendor system table of the data security knowledge base, the data corresponding to the lowest-level data security policy code is automatically or manually selected as the data security policy recommendation result; and the currently generated lowest-level data security policy code is filled in the data security policy code column of the data security policy relationship table; The recommended data security policies are ranked in ascending order, including data classification policy, data operation policy, and desensitization policy. The data classification strategy is to classify metadata into level 1, level 2 and level 3 according to the importance of metadata. Desensitization strategies are to change sensitive information in metadata through masking, hashing, truncation, or encryption operations; Data operation strategies include querying metadata and publishing metadata.

2. A data governance system based on knowledge management and identification of data security policies according to claim 1, characterized in that: The metadata collected by the data governance subsystem includes the first source data from the external system and the second source data from the data warehouse of the data governance subsystem; the first source data includes the supplier, system, version, data source type, IP, port, user and password fields; the second source data includes the schema name, table English name, table comment, field English name, field comment, data type, whether it is required and whether it is a primary key field.

3. The data governance system based on knowledge management and identification of data security policies according to claim 1 is characterized in that: The published metadata in the data operation strategy is to further classify the metadata into three categories after executing the data classification strategy: unconditional sharing, conditional sharing, and non-sharing. A desensitizing algorithm is set for conditional sharing metadata above level 2.

4. The data governance system based on knowledge management and identification of data security policies according to claim 1 is characterized in that: The further generation of the updated data security policy is that the implementation workbench adds or deletes the content of the data warehouse hierarchical column of the security protection data table in combination with the system field of the metadata after obtaining the data security policy recommendation result.

5. The data governance system based on knowledge management and identification of data security policies according to claim 4 is characterized in that: The updated data security policy is synchronized to the data governance subsystem and the data security knowledge base respectively. On the one hand, after the updated data security policy is generated, the data security policy knowledge corresponding to the updated data security policy is saved and merged into the preset supplier system table, security protection data table and data security policy relationship table of the data security knowledge base. The data security policy knowledge includes the supplier, system number, version number, data warehouse layer, data table code, database model, table English name, table Chinese name, field English name, field Chinese name and data security policy code field set in sequence. Each field of the data security policy knowledge corresponds one-to-one to the non-system number column of the supplier system table, security protection data table and data security policy relationship table.

6. A data governance method based on knowledge management to identify data security policies, configured with the data governance system based on knowledge management to identify data security policies according to any one of claims 1 to 5, characterized in that: The method comprises the following steps: S1: Collect metadata: Call the data governance subsystem to collect metadata; S2: Implement the workbench to observe the metadata obtained by the data governance subsystem and identify the metadata that needs to be added with data security policies; S3: The implementation workbench obtains data security policy recommendation results from the data security knowledge base; S4: Adjust and supplement the data security policy to obtain an updated data security policy; S5: Save the updated data security policy to the data security knowledge base; S6: Synchronize the updated data security policy to the data governance subsystem; S7: The data governance subsystem implements the updated data security policy.

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