Data compliance determination method, device, electronic device and storage medium

Abstract

The embodiments of the present application propose a data compliance determination method, device, electronic device and storage medium, which relate to the field of data analysis technology. A plurality of data to be analyzed are obtained, and metadata corresponding to each data to be analyzed are extracted; label information of each data to be analyzed is matched according to the label library and each metadata, and risk rule matching is performed on each data to be analyzed according to the label matching results corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed and the rule library; based on the label matching results and risk rule matching results corresponding to each data to be analyzed, it is determined whether each data to be analyzed has a compliance risk. In this way, the accuracy of matching risk rules can be improved. On this basis, the label matching results and risk rule matching results are combined to jointly determine whether the data to be analyzed has a compliance risk, which can improve the accuracy of compliance risk judgment.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a data compliance determination method, device, electronic device, and storage medium. Background Art

[0002] With the acceleration of digital transformation and the tightening of global data regulation, data compliance risks are becoming increasingly important in scenarios such as finance, healthcare, e-commerce, government affairs, and cross-border business. These risks include personal information protection, big data risk management, and data security. Currently, data compliance risk assessments often lack accuracy. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a data compliance determination method, device, electronic device and storage medium to improve the accuracy of data compliance risk judgment.

[0004] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0005] In a first aspect, the present application provides a data compliance determination method, which is applied to an electronic device, wherein the electronic device stores a label library and a rule library, wherein the label library stores a plurality of label information, wherein the label information represents attribute information of risk data, and the rule library stores a plurality of risk rules, wherein the method comprises:

[0006] Acquire multiple data to be analyzed, and extract metadata corresponding to each of the data to be analyzed;

[0007] Matching label information of each data to be analyzed according to the label library and each metadata, and matching risk rules of each data to be analyzed according to the label matching result corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library;

[0008] According to the label matching results and risk rule matching results corresponding to each of the data to be analyzed, it is determined whether each of the data to be analyzed has a compliance risk.

[0009] In an optional embodiment, matching label information of each data to be analyzed based on the label library and each metadata, and matching risk rules of each data to be analyzed based on the label matching result corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library, includes:

[0010] For each metadata corresponding to the data to be analyzed, determining whether there is target tag information corresponding to the metadata in the plurality of tag information in the tag library;

[0011] If there is target tag information corresponding to the metadata, performing risk rule matching on the data to be analyzed according to the target tag information and multiple risk rules in the rule base;

[0012] If the target tag information corresponding to the metadata does not exist, risk rule matching is performed on the data to be analyzed according to the metadata of the data to be analyzed and the multiple risk rules in the rule base.

[0013] In an optional embodiment, the metadata includes field information, and the label information includes a field label, a similar field label corresponding to the field label, a sensitivity label, and a category label;

[0014] The step of determining, for each metadata corresponding to each piece of data to be analyzed, whether target tag information corresponding to the metadata exists in the plurality of tag information in the tag library includes:

[0015] For each metadata corresponding to the data to be analyzed, determining whether a target field tag corresponding to the metadata exists in the plurality of field tags;

[0016] If a target field label corresponding to the metadata exists among the plurality of field labels, determining a target similarity field label, a target sensitivity label, and a target category label corresponding to the metadata according to the target field label;

[0017] If the target field tag corresponding to the metadata does not exist in the plurality of field tags, determining whether the target similar field tag corresponding to the metadata exists in the plurality of similar field tags;

[0018] If a target similar field tag corresponding to the metadata exists among the multiple similar field tags, determining the target field tag, target sensitivity tag, and target category tag corresponding to the metadata according to the target similar field tag;

[0019] If the target similar field tag corresponding to the metadata does not exist in the multiple similar field tags, it is determined that the target tag information corresponding to the metadata does not exist in the multiple tag information.

[0020] In an optional implementation manner, determining whether a target field tag corresponding to the metadata exists among the plurality of field tags includes:

[0021] Performing field value matching on the metadata and the plurality of field tags;

[0022] If the field value matches successfully, the field tag that matches the metadata among the multiple field tags is determined as the target field tag; if the field value matches fail, regular expression matching is performed on the metadata and the multiple field tags;

[0023] If the regular expression matches successfully, the field tag that matches the metadata among the multiple field tags is determined as the target field tag; if the regular expression matches unsuccessfully, similarity matching is performed on the metadata and the multiple field tags;

[0024] If the similarity matching is successful, the field tag that matches the metadata among the multiple field tags is determined as the target field tag. If the similarity matching fails, it is determined that there is no target field tag corresponding to the metadata among the multiple field tags.

[0025] In an optional embodiment, performing risk rule matching on the data to be analyzed based on the target tag information and the multiple risk rules in the rule base includes:

[0026] Determining a candidate risk rule corresponding to the target label information from the plurality of risk rules;

[0027] Performing pattern matching on the target label information and the candidate risk rules;

[0028] If the pattern matching is successful, the candidate risk rule is determined as the target risk rule; if the pattern matching fails, semantic matching is performed on the target label information and the candidate risk rule;

[0029] If the semantic matching is successful, the candidate risk rule is determined as the target risk rule; if the semantic matching fails, it is determined that the data to be analyzed does not have a corresponding target risk rule.

[0030] In an optional embodiment, performing risk rule matching on the data to be analyzed based on the metadata of the data to be analyzed and the multiple risk rules in the rule base includes:

[0031] Performing field matching on the metadata of the data to be analyzed and the plurality of risk rules;

[0032] If the field matching is successful, the risk rule that matches the metadata among the multiple risk rules is determined as the target risk rule. If the field matching fails, it is determined that there is no corresponding target risk rule for the data to be analyzed.

[0033] In an optional embodiment, determining whether each of the data to be analyzed has a compliance risk based on the label matching result and the risk rule matching result corresponding to each of the data to be analyzed includes:

[0034] If the data to be analyzed has corresponding target label information and target risk rules, it is determined that the data to be analyzed has compliance risks;

[0035] If the data to be analyzed has corresponding target label information and no corresponding target risk rule, a risk value is calculated according to the target label information corresponding to the data to be analyzed, and whether the data to be analyzed has compliance risk is determined according to the risk value;

[0036] If the data to be analyzed does not have corresponding target label information and has corresponding target risk rules, the data to be analyzed is sent to a processing personnel, and based on the processing personnel's confirmation operation on the data to be analyzed, it is determined whether the data to be analyzed has compliance risks;

[0037] If the data to be analyzed does not have corresponding target tag information and does not have corresponding target risk rules, it is determined that the data to be analyzed does not have compliance risks.

[0038] In an optional embodiment, calculating a risk value based on target tag information corresponding to the data to be analyzed, and determining whether the data to be analyzed has a compliance risk based on the risk value, includes:

[0039] Calculate the risk value based on the target sensitivity label corresponding to the data to be analyzed and a preset risk calculation formula;

[0040] If the risk value exceeds the preset risk threshold, the data to be analyzed is sent to a processing personnel, and based on the processing personnel's confirmation operation on the data to be analyzed, it is determined whether the data to be analyzed has compliance risks;

[0041] If the risk value does not exceed the preset risk threshold, it is determined that there is no compliance risk in the data to be analyzed.

[0042] In an optional embodiment, the method further comprises:

[0043] The data to be analyzed that has compliance risks is sent to the processing personnel, and the data to be analyzed is repaired or isolated according to the processing operations of the processing personnel on the data to be analyzed.

[0044] In an optional embodiment, the rule base further includes a first correction rule corresponding to each risk rule; and the data repair or data isolation of the data to be analyzed based on the processing operation of the processing personnel on the data to be analyzed includes:

[0045] Obtaining the processing operation corresponding to the data to be analyzed by the processing personnel;

[0046] If the processing operation is to perform data repair on the data to be analyzed, then if there is a corresponding target risk rule for the data to be analyzed, the data to be analyzed is corrected according to a first correction rule corresponding to the target risk rule; and if there is no corresponding target risk rule for the data to be analyzed, the data to be analyzed is corrected according to a second correction rule sent by the processing personnel;

[0047] If the processing operation is to perform data isolation on the data to be analyzed, an isolation operation is performed on the data to be analyzed according to the data type of the data to be analyzed.

[0048] In an optional embodiment, after performing data correction on the data to be analyzed, the method further includes:

[0049] Performing repair verification on the data to be analyzed according to the first correction rule or the second correction rule corresponding to the data to be analyzed;

[0050] In the case that the repair verification of the data to be analyzed fails, data restoration is performed on the repaired data to be analyzed, and the repair failure reason corresponding to the data to be analyzed is recorded.

[0051] In a second aspect, the present application provides a data compliance determination device, which is applied to an electronic device, wherein the electronic device stores a label library and a rule library, wherein the label library includes multiple label information representing attribute information of the data to be analyzed, and the rule library includes multiple risk rules, wherein the device includes:

[0052] An acquisition module, configured to acquire a plurality of data to be analyzed and extract metadata corresponding to each of the data to be analyzed;

[0053] a matching module, configured to match label information of each of the data to be analyzed according to the label library and each of the metadata, and to match risk rules of each of the data to be analyzed according to the label matching results corresponding to each of the data to be analyzed, the metadata corresponding to each of the data to be analyzed, and the rule library;

[0054] The determination module is used to determine whether each of the data to be analyzed has a compliance risk based on the label matching results and risk rule matching results corresponding to each of the data to be analyzed.

[0055] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the method described in any one of the aforementioned embodiments.

[0056] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any one of the aforementioned embodiments.

[0057] The data compliance determination method, device, electronic device and storage medium provided in the embodiments of the present application can match the label information of each data to be analyzed based on multiple label information in the label library and the metadata corresponding to the data to be analyzed, and match the risk rules of each data to be analyzed based on the label matching results, the metadata of each data to be analyzed and the multiple risk rules in the rule library, thereby determining whether each data to be analyzed has a compliance risk based on the label matching results and risk rule matching results corresponding to each data to be analyzed. In this way, the data to be analyzed is firstly matched with labels, and then the risk rules are matched with the data to be analyzed based on the label matching results, which can improve the accuracy of matching risk rules. On this basis, the label matching results and risk rule matching results are combined to jointly determine whether the data to be analyzed has a compliance risk, which can improve the accuracy of compliance risk judgment.

[0058] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 A block diagram of an electronic device provided in an embodiment of the present application is shown;

[0061] Figure 2 A schematic diagram of a process for determining data compliance provided by an embodiment of the present application is shown;

[0062] Figure 3 Another flow chart of the data compliance determination method provided in an embodiment of the present application is shown;

[0063] Figure 4 Another flow chart of the data compliance determination method provided in an embodiment of the present application is shown;

[0064] Figure 5 A functional module diagram of a data compliance determination device provided in an embodiment of the present application is shown.

[0065] Icon: 100 - memory; 110 - processor; 120 - communication module; 200 - acquisition module; 210 - matching module; 220 - determination module. DETAILED DESCRIPTION

[0066] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0067] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present application.

[0068] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0069] Please refer to Figure 1 , is a block diagram of an electronic device provided in an embodiment of the present application. The electronic device includes a memory 100, a processor 110, and a communication module 120. The memory 100, processor 110, and communication module 120 are electrically connected to each other, directly or indirectly, to enable data transmission or exchange. For example, these components may be electrically connected to each other via one or more communication buses or signal lines.

[0070] Memory 100 is used to store computer programs or data that can be executed by the processor. Memory 100 can be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0071] The processor 110 is used to read / write data or computer programs stored in the memory and execute the computer program to implement the data compliance determination method provided in the embodiment of the present application.

[0072] The communication module 120 is used to establish a communication connection between the electronic device and other communication terminals through a network, and to send and receive data through the network.

[0073] It should be understood that Figure 1 The structure shown is only a schematic diagram of the structure of the electronic device. The electronic device may also include Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0074] Optionally, the electronic device further stores a tag library and a rule library, wherein the tag library stores a plurality of tag information representing attribute information of risk data, and the rule library stores a plurality of risk rules.

[0075] Optionally, the label information is attribute information of data that may have compliance risks. For example, an ID card number may have risks such as personal information leakage, and the label information may include an ID card number label.

[0076] In this embodiment, both the label library and the rule library can be connected to some external policy supervision platform interfaces to synchronize label information and risk rules in real time to ensure the accuracy of data compliance risk assessment.

[0077] Next, the above Figure 1 The electronic device in the embodiment is the execution subject, and the data compliance determination method provided by the embodiment of the present application is exemplarily introduced in combination with the flow chart. Specifically, Figure 2A flow chart of a data compliance determination method provided in an embodiment of the present application is provided in Figure 2 , the method comprising:

[0078] Step S20: Acquire multiple data to be analyzed, and extract metadata corresponding to each data to be analyzed.

[0079] Optionally, the electronic device may obtain the data to be analyzed through various channels, such as database connection, API (Application Programming Interface) call, FTP (File Transfer Protocol) upload, etc.

[0080] Optionally, the data to be analyzed may be multi-source heterogeneous data.

[0081] In this embodiment, the electronic device may adopt different methods to obtain metadata corresponding to the data to be analyzed obtained by different acquisition methods.

[0082] In one possible implementation, for the data to be analyzed obtained through a database connection, the metadata (field name, index, comment, etc.) corresponding to the data to be analyzed can be obtained by directly reading the table schema.

[0083] For the data to be analyzed obtained through FTP upload, the file type can be determined by the file extension (such as .csv, .sql, .json, .xml, etc.) and grammatical features (such as delimiters, keywords, format tags, etc.), so as to parse the metadata of the file to obtain field names, field types, etc. Then, methods such as pandas.infer_dtype are used to sample part of the data to be analyzed to infer the field type corresponding to the data to be analyzed. The field definition corresponding to the data to be analyzed is inferred by translation or matching with pre-trained word vectors to obtain the metadata corresponding to the data to be analyzed.

[0084] For API calls, you can obtain the data to be analyzed by defining the interface address, request method, request format, etc., parse the data to be analyzed according to different request formats, and obtain metadata such as field name and field type.

[0085] Optionally, to facilitate subsequent processing, the electronic device may convert the metadata into a key-value pair format after acquiring the metadata.

[0086] In one possible implementation, the ability to analyze associations between metadata can be enhanced through knowledge graph technology.

[0087] Step S21 : matching label information of each data to be analyzed according to the label library and each metadata, and matching risk rules of each data to be analyzed according to the label matching result corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed and the rule library.

[0088] Optionally, the tag matching result may include whether the data to be analyzed matches corresponding target tag information and specific target tag information that is matched.

[0089] In this embodiment, the electronic device may perform risk rule matching on the data to be analyzed in combination with the tag matching result corresponding to the data to be analyzed, thereby improving the accuracy of risk rule matching.

[0090] Step S22 : determining whether each data to be analyzed has a compliance risk based on the tag matching results and risk rule matching results corresponding to each data to be analyzed.

[0091] Optionally, the risk rule matching result may include whether the data to be analyzed matches a corresponding target risk rule and the specific target risk rule matched.

[0092] It can be understood that compared with the method in the related art of simply matching the data to be analyzed with the risk rules to determine whether the data to be analyzed has compliance risks, the data compliance method provided in the embodiment of the present application can perform label matching on the data to be analyzed and then perform risk rule matching on the data to be analyzed based on the label matching results, thereby combining the label matching results and the risk rule matching results to determine whether the data to be analyzed has compliance risks. This not only ensures the accuracy of the risk rule matching, but also improves the accuracy of compliance risk judgment by combining the label matching results and the risk rule matching results to jointly determine whether the data to be analyzed has compliance risks.

[0093] In the data compliance determination method provided by the embodiment of the present application, the electronic device can match the label information of each data to be analyzed based on multiple label information in the label library and the metadata corresponding to the data to be analyzed, and match the risk rules of each data to be analyzed based on the label matching results, the metadata of each data to be analyzed, and the multiple risk rules in the rule library, thereby determining whether each data to be analyzed has a compliance risk based on the label matching results and risk rule matching results corresponding to each data to be analyzed. In this way, the data to be analyzed is first labeled and then matched with the risk rules based on the label matching results, which can improve the accuracy of matching risk rules. On this basis, the label matching results and risk rule matching results are combined to jointly determine whether the data to be analyzed has a compliance risk, which can improve the accuracy of compliance risk judgment.

[0094] Optionally, a possible implementation method is provided for how to match label information of each data to be analyzed based on the label library and each metadata, and match risk rules of each data to be analyzed based on the label matching results corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library.

[0095] Specifically, in Figure 2 On the basis of Figure 3 For another flow chart of the data compliance determination method provided in the embodiment of the present application, please refer to Figure 3 , the above step S21 can be implemented by the following steps:

[0096] Step S21 - 1 : for each metadata corresponding to each piece of data to be analyzed, determine whether there is target tag information corresponding to the metadata in the multiple tag information in the tag library.

[0097] Optionally, the electronic device may determine whether target tag information corresponding to the metadata exists in the plurality of tag information in the tag library by matching the metadata with the tag information.

[0098] Step S21 - 2 : If there is target tag information corresponding to the metadata, risk rule matching is performed on the data to be analyzed according to the target tag information and multiple risk rules in the rule library.

[0099] It can be understood that each risk rule in the rule base is generated according to the label information, so the risk rule matching can be performed in the rule base according to the target label information corresponding to the data to be analyzed.

[0100] Step S21 - 3 : If there is no target tag information corresponding to the metadata, risk rule matching is performed on the data to be analyzed according to the metadata of the data to be analyzed and the multiple risk rules in the rule library.

[0101] It is understandable that if the metadata does not have a target tag, risk rule matching can be performed directly in the rule library based on the metadata corresponding to the data to be analyzed.

[0102] Optionally, there may be multiple pieces of label information.

[0103] In this embodiment, for ease of management, the tag library may have a hierarchical structure, and the tag information may include multi-level tags.

[0104] Optionally, the label information may include a field label, a similar field label corresponding to the field label, a sensitivity label, and a category label.

[0105] Optionally, the sensitivity label may include non-sensitive, low-sensitive, moderately sensitive, and extremely sensitive, wherein each sensitivity label may be represented by a number, for example, non-sensitive is represented as 1, low-sensitive is represented as 2, moderately sensitive is represented as 3, and extremely sensitive is represented as 4.

[0106] In one possible implementation, the label information may include a first-level label, a second-level label, a third-level label, a fourth-level label, a fifth-level label and a sixth-level label, wherein the first-level label is a broad category label, the second-level label is a refined label of the first-level label, the third-level label is a refined label of the second-level label, the fifth-level label is a refined label of the third-level label, the sixth-level label is a label of the same level as the fifth-level label, and corresponds one-to-one with the fifth-level label, the fourth-level label is a sensitive label, and the fourth-level label may correspond one-to-one with the third-level label.

[0107] Optionally, the fifth-level label may be a specific field label, the sixth-level label may be a similar field label corresponding to the field label, and the first-level label to the third-level label are all category labels.

[0108] In one example, if the first-level label is personal information, the second-level label refined under the first-level label may include personal financial information, and the third-level label refined under the personal financial information in the second-level label may include bank account information. The fourth-level label corresponding to the third-level label, that is, the sensitive label can be a high sensitivity level, and the fifth-level label refined under the bank account information in the third-level label, that is, the specific field label can be the main account number, and the sixth-level label corresponding to the field label, that is, the similar field label can be a bank account number, bank card number, debit card number, credit card number, etc., and there can be multiple sixth-level labels.

[0109] It can be understood that the specific field label can be the field attribute information of the standard name, such as identity information, and the similar fields are other names corresponding to the field attribute information of the standard name. For example, some companies directly call the identity information identity ID for ease of processing.

[0110] In this embodiment, the category labels include primary labels, secondary labels, and tertiary labels.

[0111] Based on this, a possible implementation method is provided below for determining whether target tag information corresponding to each metadata corresponding to each piece of data to be analyzed exists in multiple tag information in the tag library.

[0112] Specifically, the electronic device may determine, for each metadata corresponding to each piece of data to be analyzed, whether there is a target field tag corresponding to the metadata in a plurality of field tags.

[0113] In this embodiment, if there is a target field tag corresponding to the metadata among multiple field tags, the target similar field tag, target sensitivity tag and target category tag corresponding to the metadata are determined based on the target field tag; if there is no target field tag corresponding to the metadata among multiple field tags, it is determined whether there is a target similar field tag corresponding to the metadata among multiple similar field tags; if there is a target similar field tag corresponding to the metadata among multiple similar field tags, the target field tag, target sensitivity tag and target category tag corresponding to the metadata are determined based on the target similar field tag; if there is no target similar field tag corresponding to the metadata among multiple similar field tags, it is determined that there is no target tag information corresponding to the metadata among the multiple tag information.

[0114] Optionally, when performing label matching, metadata may be matched with specific field labels first. If the specific field label matching fails, metadata may be matched with similar field labels.

[0115] It can be understood that since there is a corresponding relationship between the first-level label to the sixth-level label, after determining the target field label (fifth-level label) corresponding to the metadata, other label information corresponding to the metadata can be directly obtained.

[0116] Similarly, if the metadata does not directly match the fifth-level tag, but matches the target similar field (sixth-level tag) corresponding to the metadata, then other tag information corresponding to the metadata can also be directly obtained based on the sixth-level tag.

[0117] In this embodiment, if both the fifth-level tag and the sixth-level tag fail to match, it can be determined that the metadata has no corresponding target tag information.

[0118] Optionally, in order to improve the success rate of label matching and thus further improve the accuracy of data compliance risk determination, multiple matching methods can be used to match metadata and field labels as well as similar field labels.

[0119] Next, we take field tags as an example to describe how to determine whether there is a target field tag corresponding to the metadata among multiple field tags.

[0120] Specifically, the electronic device may first perform field value matching on the metadata and multiple field tags. If the field value matching is successful, the field tag among the multiple field tags that matches the metadata is determined as the target field tag. If the field value matching fails, the metadata and multiple field tags are matched by regular expressions. If the regular expression matching is successful, the field tag among the multiple field tags that matches the metadata is determined as the target field tag. If the regular expression matching fails, the metadata and multiple field tags are matched by similarity. If the similarity matching is successful, the field tag among the multiple field tags that matches the metadata is determined as the target field tag. If the similarity matching fails, it is determined that there is no target field tag corresponding to the metadata among the multiple field tags.

[0121] Optionally, field value matching refers to calculating the field value of the metadata and the field value of each field label, and determining whether the field value of the metadata is equal to the field value of the field label. If they are equal, it means that the field value matching is successful, and the field label equal to the metadata field value is determined as the target field label.

[0122] Optionally, if there is no field label that is equal to the metadata field value, it can be determined that the field value matching fails. At this time, the regular expression of the metadata and the regular expressions of each field label can be calculated to determine whether the regular expression of the metadata matches the regular expression of each field label, and the field label that matches the regular expression of the metadata is determined as the target field label.

[0123] Optionally, if there is no field label that matches the metadata regular expression, it can be determined that the regular expression matching fails. In this case, the cosine similarity between the metadata and each field label can be calculated.

[0124] In one possible implementation, when calculating cosine similarity, the metadata and field labels can be first processed through the Sentence-BERT model to generate a 768-dimensional vector corresponding to the metadata and a 768-dimensional vector corresponding to the field label. The cosine similarity between the metadata and each field label is then calculated using a preset similarity calculation formula.

[0125] Optionally, the similarity calculation formula may be S=(A·B) / (||A||*||B||), where S represents cosine similarity, A represents the 768-dimensional vector corresponding to the metadata, and B represents the 768-dimensional vector corresponding to the field label.

[0126] Optionally, the electronic device may determine field tags whose cosine similarity with the metadata exceeds a first preset similarity threshold. If there are multiple field tags exceeding the first preset similarity threshold, then there are multiple target field tags corresponding to the metadata.

[0127] Optionally, if there is no field tag whose cosine similarity with the metadata exceeds a first preset similarity threshold, it can be determined that the metadata does not have a corresponding target field tag.

[0128] In one possible implementation, the first preset similarity threshold may be 0.7. It is understandable that the process of determining whether there is a target similar field tag corresponding to metadata among multiple similar field tags is consistent with the above process and will not be elaborated on here.

[0129] In this embodiment, if target tag information exists, risk rule matching can be performed on the data to be analyzed based on the target tag information.

[0130] Optionally, in order to improve the success rate of risk rule matching and thus further improve the accuracy of data compliance risk determination, multiple matching methods may be used for risk rule matching.

[0131] In this embodiment, the electronic device may first determine the candidate risk rule corresponding to the target tag information from multiple risk rules, and perform pattern matching on the target tag information and the candidate risk rule. If the pattern matching is successful, the candidate risk rule is determined as the target risk rule. If the pattern matching fails, the target tag information and the candidate risk rule are semantically matched. If the semantic matching is successful, the candidate risk rule is determined as the target risk rule. If the semantic matching fails, it is determined that there is no corresponding target risk rule for the data to be analyzed.

[0132] Optionally, since each risk rule in the rule base is generated according to label information, a candidate risk rule can be directly determined from a plurality of risk rules according to target label information corresponding to the data to be analyzed.

[0133] In one possible implementation, the corresponding candidate risk rules can be directly determined based on the refined five-level labels. For example, if the target label information is refined to the five-level label of a personal ID number, the candidate risk rule is the risk rule corresponding to the personal ID number.

[0134] Optionally, pattern matching refers to matching through regular expressions or data templates. In this embodiment, the data template can be a data format.

[0135] Optionally, the electronic device may calculate the regular expression corresponding to the target tag information and the regular expression corresponding to the candidate risk rule, and determine whether the candidate rule is the target risk rule by comparing whether the regular expressions of the two match. Alternatively, the electronic device may compare the data template corresponding to the target tag information and the data template corresponding to the candidate risk rule to determine whether the candidate risk rule is the target risk rule.

[0136] Optionally, if the candidate risk rule does not match the regular expression of the target label information, or does not match the data template corresponding to the target label information, it is necessary to perform semantic matching between the target label information and the candidate risk rule.

[0137] Optionally, the electronic device may extract the text description corresponding to the target tag information, generate a semantic vector corresponding to the target tag information based on the text description, and extract the semantic features corresponding to the candidate risk rule, including the text description, etc., generate the semantic vector corresponding to the candidate risk rule through the Sentence-BERT model, and then calculate the cosine similarity between the semantic vector corresponding to the target tag information and the semantic vector corresponding to the candidate risk rule, thereby determining whether the candidate risk rule is the target risk rule based on the cosine similarity.

[0138] Optionally, the cosine similarity is calculated in the same manner as described above, and will not be described in detail here.

[0139] Optionally, the electronic device may determine the candidate risk rule as the target risk rule if the cosine similarity between the candidate risk rule and the target tag information exceeds a second preset similarity threshold. It can be understood that if the cosine similarity between the candidate risk rule and the target tag information does not exceed the second preset similarity threshold, it can be determined that there is no target risk rule corresponding to the target tag information.

[0140] In a possible implementation, the second preset similarity threshold may be 0.8.

[0141] Optionally, if there is no target tag information, field matching can be performed directly on the metadata of the data to be analyzed and multiple risk rules. If the field matching is successful, the risk rule that matches the metadata among the multiple risk rules is determined as the target risk rule. If the field matching fails, it is determined that there is no corresponding target risk rule for the data to be analyzed.

[0142] In this embodiment, to improve matching efficiency, all rules in the rule library are imported into the rule engine. During matching, the rule engine automatically traverses the rules and calls different algorithms to match risk rules. Next, we provide a possible implementation method for determining whether each piece of data to be analyzed presents compliance risks based on the corresponding label matching results and risk rule matching results.

[0143] Specifically, if the data to be analyzed has corresponding target tag information and target risk rules, the electronic device can determine that the data to be analyzed has compliance risks. If the data to be analyzed does not have corresponding target tag information and does not have corresponding target risk rules, the electronic device determines that the data to be analyzed does not have compliance risks. If the data to be analyzed only matches one of the target tag information and target risk rules, further judgment is required on the data.

[0144] In one possible implementation, if the data to be analyzed has corresponding target label information and no corresponding target risk rule, a risk value is calculated based on the target label information corresponding to the data to be analyzed, and whether the data to be analyzed has compliance risks is determined based on the risk value.

[0145] In another possible implementation method, if the data to be analyzed does not have corresponding target label information and there is a corresponding target risk rule, the data to be analyzed is sent to the processing personnel, and based on the processing personnel's confirmation operation on the data to be analyzed, it is determined whether the data to be analyzed has compliance risks.

[0146] In this embodiment, the electronic device needs to calculate a risk value for the data to be analyzed that matches the target tag information. The risk value can represent the probability that the data to be analyzed has a compliance risk.

[0147] Optionally, when the data to be analyzed does not have target label information but has target risk rules, the risk value cannot be calculated, so the rules to be analyzed can be sent directly to the processing personnel for risk judgment.

[0148] Next, we provide a possible implementation method for calculating the risk value based on the target label information corresponding to the data to be analyzed, and determining whether the data to be analyzed has compliance risks based on the risk value.

[0149] In this embodiment, the electronic device can calculate the risk value based on the target sensitive label corresponding to the data to be analyzed and the preset risk calculation formula. If the risk value exceeds the preset risk threshold, the electronic device can send the data to be analyzed to the processing personnel, and determine whether the data to be analyzed has compliance risks based on the processing personnel's confirmation operation on the data to be analyzed. If the risk value does not exceed the preset risk threshold, it is determined that the data to be analyzed does not have compliance risks.

[0150] In one possible implementation, the risk calculation formula can be represented as:

[0151] RiskScore= *MatchRate+ *Sensitivity+ *HistoryViolation

[0152] Among them, RiskScore represents the risk value, MatchRate represents the cosine similarity between the candidate risk rule and the target label information, Sensitivity represents the sensitive parameter corresponding to the sensitive label in the target label information, and HistoryViolation represents the ratio of the total number of historical violations of the data source corresponding to the data to be analyzed to the total number of historical violations of all data sources.

[0153] Optionally, the sensitive parameter can be set according to the actual application situation and the specific sensitivity label. For example, the sensitive parameter corresponding to non-sensitive is 25%, the sensitive parameter corresponding to low sensitivity is 50%, the sensitive parameter corresponding to moderate sensitivity is 75%, and the sensitive parameter corresponding to extremely sensitive is 100%.

[0154] Optionally, 、 、 Respectively represent constant values, which can be set according to actual application conditions. In one possible implementation, 、 、 .

[0155] Optionally, the preset risk threshold can be set according to actual application conditions, for example, to 0.6.

[0156] Optionally, considering that in related technologies, data with compliance risks are often discarded or globally isolated, resulting in a decrease in data utilization, in order to improve data utilization, electronic devices can also repair data with compliance risks and isolate data that cannot be repaired.

[0157] Specifically, for data to be analyzed that poses compliance risks, the electronic device can also send it to a processing personnel, who will determine whether the data can be repaired. The electronic device can then perform data repair or data isolation on the data to be analyzed based on the processing operations performed by the processing personnel on the data to be analyzed.

[0158] In this embodiment, for data to be analyzed that requires a processing personnel to determine whether it has a compliance risk, the processing personnel can determine whether the data to be analyzed can be repaired if it is determined that the data to be analyzed has a compliance risk, and thus issue a processing instruction for the data to be analyzed; and for data to be analyzed that is directly determined by the electronic device to have a compliance risk, the data to be analyzed can be sent to the processing personnel to determine whether it can be repaired.

[0159] In this embodiment, the processing personnel may send a repair instruction for the data to be analyzed to the electronic device if it is determined that the data to be analyzed can be repaired, and the electronic device may then execute a data repair process for the data to be analyzed. If the processing personnel determines that the data to be analyzed cannot be repaired, an isolation instruction for the data to be analyzed may be sent to the electronic device, and the electronic device may perform data isolation on the data to be analyzed.

[0160] As can be appreciated, since the data repair process can be performed autonomously by the electronic device, eliminating the need for manual data repair by personnel, this can improve data utilization while reducing manual processing costs and increasing data processing efficiency. In one possible implementation, to facilitate data repair, the rule library can also include a first correction rule corresponding to each risk rule.

[0161] It can be understood that each risk rule corresponds to a first correction rule. For example, if the ID card number should be desensitized according to a certain format under compliance conditions, the risk rule can be the format when the ID card number is not desensitized or when the desensitization is incorrect. The first correction rule corresponding to the risk rule is the format of the ID card number after normal desensitization. In this case, the electronic device can process the ID card number with compliance risks based on the first correction rule to make it compliant.

[0162] In this embodiment, the electronic device can obtain the processing operations corresponding to the data to be analyzed by the processing personnel.

[0163] In one possible implementation, if the processing operation is to perform data repair on the data to be analyzed, then if there is a corresponding target risk rule for the data to be analyzed, the data to be analyzed is corrected according to a first correction rule corresponding to the target risk rule; and if there is no corresponding target risk rule for the data to be analyzed, the data to be analyzed is corrected according to a second correction rule sent by the processing personnel.

[0164] In another possible implementation, if the processing operation is to isolate the data to be analyzed, the isolation operation is performed on the data to be analyzed according to the data type of the data to be analyzed.

[0165] Optionally, if the data to be analyzed that can be repaired matches the corresponding target risk rule, the electronic device can directly call the first correction rule corresponding to the target risk rule to correct the data to be analyzed.

[0166] If the data to be analyzed that can be repaired does not match the corresponding target risk rule, the electronic device can send the data to be analyzed to the relevant processing personnel, who can define a second correction rule for the data to be analyzed.

[0167] It is understandable that the electronic device can use NLP and other technologies to segment and tag the second correction rule, and based on the understanding of the user's intention, determine the type of user demand through semantic analysis, that is, the specific correction content, and then generate executable code logic according to the data source type and data connection information of the data to be analyzed, so as to achieve data repair for the data to be analyzed by executing the code logic.

[0168] Optionally, the data type of the data to be analyzed refers to a data format, including, for example, structured data, file-type data, API-type data, and the like.

[0169] In this embodiment, when the data to be analyzed cannot be repaired, the electronic device can isolate the data according to its data type.

[0170] In one possible implementation, if the data to be analyzed is structured data, such as data in a structured database table, the electronic device can use the SQL syntax REVOKE statement to revoke all users' SELECT, INSERT, UPDATE, and DELETE permissions on the specified table; if the data to be analyzed is file-type data, the electronic device can use the AES algorithm to encrypt the file to prevent users from adding, deleting, modifying, or querying the file; if the data to be analyzed is API-type data, the electronic device can automatically establish a blacklist and whitelist mechanism through a dynamic routing engine deployed at the API gateway layer, update prohibited API paths in real time, and prevent users from obtaining non-compliant API data.

[0171] In this embodiment, the electronic device may trigger an alarm after successfully isolating the data to notify the relevant person in charge to manually process the isolated data to be analyzed.

[0172] Optionally, considering that there may still be repair errors or repair failures after the data is repaired, the electronic device can also perform repair verification on the data to be analyzed according to the first correction rule or the second correction rule corresponding to the data to be analyzed after the data correction is performed on the data to be analyzed. If the repair verification of the data to be analyzed fails, the repaired data to be analyzed is restored, and the reason for the repair failure corresponding to the data to be analyzed is recorded.

[0173] Optionally, if the data to be analyzed is repaired by the first correction rule, the electronic device can perform repair verification on the data to be analyzed by the first correction rule, and if the data to be analyzed is repaired by the second correction rule, the electronic device can perform repair verification on the data to be analyzed by the second correction rule.

[0174] It can be understood that if the repair is successful, the data to be analyzed after the repair can be determined to be compliant data. Conversely, if the repair fails, the data to be analyzed may not be repaired, so the electronic device can restore it to the data before the repair and record the reason for the repair failure.

[0175] Optionally, the electronic device may record the data copy generated for the data to be analyzed before repairing the data to be analyzed, so as to restore the data copy in case of repair failure.

[0176] Optionally, if the data to be analyzed fails to be repaired, the electronic device can isolate the data to be analyzed, or trigger an alarm to notify processing personnel to manually repair the data to be analyzed. Optionally, to ensure data security, the electronic device can use multi-party secure computing (MPC) technology during the data repair process to achieve the availability of the data to be analyzed without being visible.

[0177] Optionally, considering that there is still a problem in related technologies that the data compliance determination process lacks records and is difficult to meet the full-chain audit requirements of regulatory agencies, the electronic device can also generate corresponding audit records and repair logs for the data to be analyzed.

[0178] In this embodiment, the audit record may include relevant records of all steps in the entire process, including metadata extraction of the data to be analyzed, label matching, rule matching, risk calculation, compliance determination, data repair, data isolation, alarm, manual processing, etc.

[0179] Optionally, you can also combine audit records to generate multi-dimensional statistical reports for the data to be analyzed to demonstrate the overall compliance status of the data, such as compliance rate trends, frequent violation types, and remediation success rates for all analyzed data. Compliance rate trends can be statistically analyzed and displayed by date, such as day, week, or month. Frequent violation types can be displayed as percentages, and remediation success rates can be statistically analyzed by data source.

[0180] Next, combine Figure 4 The data compliance determination method provided in the embodiments of the present application is exemplarily introduced.

[0181] Specifically, Figure 4 For another flow chart of the data compliance determination method provided in the embodiment of the present application, please refer to Figure 4 .

[0182] The electronic device can first obtain the data to be analyzed and perform metadata extraction and other related processing on the data to be analyzed. Then, it matches the label information of the data to be analyzed through the label library, thereby outputting the data to be analyzed that has matched the target label information and the data to be analyzed that has not matched the target label information, and performs risk rule matching on these two types of data to be analyzed respectively.

[0183] For the data to be analyzed that matches the target tag information, it can be determined whether the data to be analyzed matches the target risk rule based on the target tag information.

[0184] If the target risk rule is matched, the data to be analyzed can be sent to the processing personnel to determine whether it can be intelligently repaired. If it can be intelligently repaired, the intelligent repair plan will be executed. If it cannot be intelligently repaired, the data to be analyzed can be isolated and relevant personnel will be notified to execute manual processing procedures, such as manual repair.

[0185] If the target risk rule is not matched, the risk value of the data to be analyzed can be calculated to determine whether the risk value of the data to be analyzed exceeds the preset risk threshold. If it does not exceed the preset risk threshold, it can be determined that there is no relevant compliance risk in the data to be analyzed, and the overall audit record and report corresponding to the data to be analyzed can be generated. If it exceeds the preset risk threshold, it can enter the manual review process and be handed over to the processing personnel to make a compliance judgment on it, and the next processing process will be executed according to the compliance judgment result.

[0186] For the data to be analyzed that does not match the target label information, risk rules can be matched directly.

[0187] If the target risk rules are matched, the data will enter the manual review process, where the processing personnel will conduct a compliance assessment and execute the next processing steps based on the compliance assessment results. If the target risk rules are not matched, the data to be analyzed will be determined to have no relevant compliance risks, and a comprehensive audit record and report will be generated for the data to be analyzed.

[0188] During the manual review process, the processing personnel can determine whether there is a compliance risk in the data to be analyzed. If it is determined that there is no compliance risk, the audit records and reports corresponding to the data to be analyzed can be generated. If it is determined that there is a compliance risk, the processing personnel can continue to determine whether the data to be analyzed can be intelligently repaired.

[0189] If intelligent repair is possible, a repair instruction can be given, and the electronic device creates a data copy for the data to be analyzed for storage. The data to be analyzed is corrected using the correction rule (the first correction rule or the second correction rule), and the correction result is verified. If the correction is successful, a repair log for the data to be analyzed can be generated, and then an overall audit record and report corresponding to the data to be analyzed can be generated. If the correction fails, the data to be analyzed that failed to be corrected can be deleted, and the data to be analyzed can be restored according to the data copy and sent to relevant personnel to execute manual processing procedures, such as manual repair.

[0190] If intelligent repair is not possible, an isolation instruction can be given, and the electronic equipment will isolate the data to be analyzed, and notify relevant personnel to execute manual processing procedures, such as manual repair, etc.

[0191] After the manual processing process is completed, it is also necessary to generate overall audit records and reports for the data to be analyzed.

[0192] In order to execute the corresponding steps in the above embodiments and various possible methods, a method for implementing a data compliance determination device is given below. Optionally, the data compliance determination device can adopt the above Figure 1 For further information, please refer to Figure 5 , Figure 5 This is a functional module diagram of a data compliance determination device provided in an embodiment of the present application. It should be noted that the basic principles and technical effects of the data compliance determination device provided in this embodiment are the same as those of the above-mentioned embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above-mentioned embodiments. The data compliance determination device includes: an acquisition module 200, a matching module 210, and a determination module 220.

[0193] The acquisition module 200 is used to acquire a plurality of data to be analyzed and extract metadata corresponding to each data to be analyzed.

[0194] It is understandable that the acquisition module 200 can also be used to execute the above step S20.

[0195] The matching module 210 is used to match the label information of each data to be analyzed according to the label library and each metadata, and to match the risk rules of each data to be analyzed according to the label matching results corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library.

[0196] It is understandable that the matching module 210 can also be used to execute the above step S21.

[0197] The determination module 220 is configured to determine whether each piece of data to be analyzed has a compliance risk based on the label matching results and the risk rule matching results corresponding to each piece of data to be analyzed.

[0198] It is understandable that the determination module 220 can also be used to execute the above step S22.

[0199] Optionally, the matching module 210 is also used to determine, for each metadata corresponding to each data to be analyzed, whether there is target tag information corresponding to the metadata in the multiple tag information in the tag library; if there is target tag information corresponding to the metadata, risk rule matching is performed on the data to be analyzed based on the target tag information and multiple risk rules in the rule library; if there is no target tag information corresponding to the metadata, risk rule matching is performed on the data to be analyzed based on the metadata of the data to be analyzed and multiple risk rules in the rule library.

[0200] It is understandable that the matching module 210 can also be used to execute the above steps S21 - 1 to S21 - 2.

[0201] Optionally, the matching module 210 is further used to determine, for each metadata corresponding to each data to be analyzed, whether there is a target field label corresponding to the metadata in multiple field labels; if there is a target field label corresponding to the metadata in multiple field labels, then determine the target similar field label, target sensitivity label and target category label corresponding to the metadata based on the target field label; if there is no target field label corresponding to the metadata in multiple field labels, then determine whether there is a target similar field label corresponding to the metadata in multiple similar field labels; if there is a target similar field label corresponding to the metadata in multiple similar field labels, then determine the target field label, target sensitivity label and target category label corresponding to the metadata based on the target similar field label; if there is no target similar field label corresponding to the metadata in multiple similar field labels, then determine that there is no target label information corresponding to the metadata in multiple label information.

[0202] Optionally, the matching module 210 is also used to perform field value matching on metadata and multiple field tags; if the field value matching is successful, the field tag that matches the metadata in the multiple field tags is determined as the target field tag; if the field value matching fails, regular expression matching is performed on the metadata and the multiple field tags; if the regular expression matching is successful, the field tag that matches the metadata in the multiple field tags is determined as the target field tag; if the regular expression matching fails, similarity matching is performed on the metadata and the multiple field tags; if the similarity matching is successful, the field tag that matches the metadata in the multiple field tags is determined as the target field tag; if the similarity matching fails, it is determined that there is no target field tag corresponding to the metadata in the multiple field tags.

[0203] Optionally, the matching module 210 is also used to determine the candidate risk rule corresponding to the target label information from multiple risk rules; perform pattern matching on the target label information and the candidate risk rules; if the pattern matching is successful, determine the candidate risk rule as the target risk rule; if the pattern matching fails, perform semantic matching on the target label information and the candidate risk rule; if the semantic matching is successful, determine the candidate risk rule as the target risk rule; if the semantic matching fails, determine that there is no corresponding target risk rule for the data to be analyzed.

[0204] Optionally, the matching module 210 is also used to perform field matching on the metadata of the data to be analyzed and multiple risk rules; if the field matching is successful, the risk rule that matches the metadata among the multiple risk rules is determined as the target risk rule; if the field matching fails, it is determined that there is no corresponding target risk rule for the data to be analyzed.

[0205] Optionally, the determination module 220 is also used to determine whether the data to be analyzed has a compliance risk if there is corresponding target label information and target risk rules for the data to be analyzed; if there is corresponding target label information for the data to be analyzed and no corresponding target risk rules, calculate the risk value based on the target label information corresponding to the data to be analyzed, and determine whether the data to be analyzed has a compliance risk based on the risk value; if there is no corresponding target label information for the data to be analyzed and there is a corresponding target risk rule, send the data to be analyzed to the processing personnel, and determine whether the data to be analyzed has a compliance risk based on the processing personnel's confirmation operation on the data to be analyzed; if there is no corresponding target label information for the data to be analyzed and no corresponding target risk rules, determine that the data to be analyzed does not have a compliance risk.

[0206] Optionally, the determination module 220 is also used to calculate the risk value based on the target sensitive label corresponding to the data to be analyzed and a preset risk calculation formula; if the risk value exceeds the preset risk threshold, the data to be analyzed is sent to the processing personnel, and based on the processing personnel's confirmation operation on the data to be analyzed, it is determined whether the data to be analyzed has compliance risks; if the risk value does not exceed the preset risk threshold, it is determined that the data to be analyzed does not have compliance risks.

[0207] Optionally, the determination module 220 is further configured to send the data to be analyzed that has compliance risks to a processing personnel, and perform data repair or data isolation on the data to be analyzed based on the processing operations performed by the processing personnel on the data to be analyzed.

[0208] Optionally, the determination module 220 is also used to obtain the processing operation corresponding to the data to be analyzed by the processing personnel; if the processing operation is to perform data repair on the data to be analyzed, then when there is a corresponding target risk rule for the data to be analyzed, the data to be analyzed is corrected according to the first correction rule corresponding to the target risk rule; and when there is no corresponding target risk rule for the data to be analyzed, the data to be analyzed is corrected according to the second correction rule sent by the processing personnel; if the processing operation is to perform data isolation on the data to be analyzed, the data to be analyzed is isolated according to the data type of the data to be analyzed.

[0209] Optionally, the determination module 220 is further configured to perform repair verification on the data to be analyzed according to the first correction rule or the second correction rule corresponding to the data to be analyzed; if the repair verification of the data to be analyzed fails, restore the repaired data to be analyzed and record the repair failure reason corresponding to the data to be analyzed.

[0210] The data compliance determination device provided in the embodiment of the present application obtains multiple data to be analyzed through an acquisition module and extracts metadata corresponding to each data to be analyzed; matches label information of each data to be analyzed based on the label library and each metadata through a matching module, and matches risk rules for each data to be analyzed based on the label matching results corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library; determines whether each data to be analyzed has a compliance risk based on the label matching results and risk rule matching results corresponding to each data to be analyzed through a determination module. In this way, label matching is first performed on the data to be analyzed, and then risk rule matching is performed on the data to be analyzed based on the label matching results, which can improve the accuracy of matching risk rules. On this basis, the label matching results and risk rule matching results are combined to jointly determine whether the data to be analyzed has a compliance risk, which can improve the accuracy of compliance risk judgment.

[0211] Optionally, the above modules can be stored in the form of software or firmware. Figure 1 The memory shown in FIG. 1 or the operating system (OS) of the electronic device may be fixed therein and may be Figure 1 Meanwhile, the data and program codes required to execute the above modules may be stored in the memory.

[0212] An embodiment of the present application also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the data compliance determination method provided in the embodiment of the present application is implemented.

[0213] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the devices, methods, and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a portion of code, and the module, program segment, or a portion of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0214] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0215] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0216] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A data compliance determination method, characterized in that: Applied to an electronic device, the electronic device stores a label library and a rule library, the label library stores multiple label information, the label information represents attribute information of risk data, and the rule library stores multiple risk rules, the method includes: Acquire multiple data to be analyzed, and extract metadata corresponding to each of the data to be analyzed; Matching label information of each data to be analyzed according to the label library and each metadata, and matching risk rules of each data to be analyzed according to the label matching result corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library; If the data to be analyzed has corresponding target label information and target risk rules, it is determined that the data to be analyzed has compliance risks; If the data to be analyzed has corresponding target label information and no corresponding target risk rule, a risk value is calculated according to the target label information corresponding to the data to be analyzed, and whether the data to be analyzed has compliance risk is determined according to the risk value; If the data to be analyzed does not have corresponding target label information and has corresponding target risk rules, the data to be analyzed is sent to a processing personnel, and based on the processing personnel's confirmation operation on the data to be analyzed, it is determined whether the data to be analyzed has compliance risks; If the data to be analyzed does not have corresponding target tag information and does not have corresponding target risk rules, it is determined that the data to be analyzed does not have compliance risks.

2. The method according to claim 1, characterized in that The step of matching label information of each data to be analyzed based on the label library and each metadata, and matching risk rules of each data to be analyzed based on the label matching result corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library, includes: For each metadata corresponding to the data to be analyzed, determining whether there is target tag information corresponding to the metadata in the plurality of tag information in the tag library; If there is target tag information corresponding to the metadata, performing risk rule matching on the data to be analyzed according to the target tag information and multiple risk rules in the rule base; If the target tag information corresponding to the metadata does not exist, risk rule matching is performed on the data to be analyzed according to the metadata of the data to be analyzed and the multiple risk rules in the rule base.

3. The method according to claim 2, characterized in that The metadata includes field information, and the tag information includes a field tag, a similar field tag corresponding to the field tag, a sensitivity tag, and a category tag; The step of determining, for each metadata corresponding to each piece of data to be analyzed, whether target tag information corresponding to the metadata exists in the plurality of tag information in the tag library includes: For each metadata corresponding to the data to be analyzed, determining whether a target field tag corresponding to the metadata exists in the plurality of field tags; If a target field label corresponding to the metadata exists among the plurality of field labels, determining a target similarity field label, a target sensitivity label, and a target category label corresponding to the metadata according to the target field label; If the target field tag corresponding to the metadata does not exist in the plurality of field tags, determining whether the target similar field tag corresponding to the metadata exists in the plurality of similar field tags; If a target similar field tag corresponding to the metadata exists among the multiple similar field tags, determining the target field tag, target sensitivity tag, and target category tag corresponding to the metadata according to the target similar field tag; If the target similar field tag corresponding to the metadata does not exist in the multiple similar field tags, it is determined that the target tag information corresponding to the metadata does not exist in the multiple tag information.

4. The method according to claim 3, characterized in that The determining whether a target field tag corresponding to the metadata exists among the plurality of field tags includes: Performing field value matching on the metadata and the plurality of field tags; If the field value matches successfully, the field tag that matches the metadata among the multiple field tags is determined as the target field tag; if the field value matches fail, regular expression matching is performed on the metadata and the multiple field tags; If the regular expression matches successfully, the field tag that matches the metadata among the multiple field tags is determined as the target field tag; if the regular expression matches unsuccessfully, similarity matching is performed on the metadata and the multiple field tags; If the similarity matching is successful, the field tag that matches the metadata among the multiple field tags is determined as the target field tag. If the similarity matching fails, it is determined that there is no target field tag corresponding to the metadata among the multiple field tags.

5. The method according to claim 2, characterized in that The performing risk rule matching on the data to be analyzed according to the target tag information and the multiple risk rules in the rule base includes: Determining a candidate risk rule corresponding to the target label information from the plurality of risk rules; Performing pattern matching on the target label information and the candidate risk rules; If the pattern matching is successful, the candidate risk rule is determined as the target risk rule; if the pattern matching fails, semantic matching is performed on the target label information and the candidate risk rule; If the semantic matching succeeds, the candidate risk rule is determined as the target risk rule. If the semantic matching fails, it is determined that the data to be analyzed does not have a corresponding target risk rule.

6. The method according to claim 2, characterized in that The performing risk rule matching on the data to be analyzed based on the metadata of the data to be analyzed and the multiple risk rules in the rule base includes: Performing field matching on the metadata of the data to be analyzed and the plurality of risk rules; If the field matching is successful, the risk rule that matches the metadata among the multiple risk rules is determined as the target risk rule. If the field matching fails, it is determined that there is no corresponding target risk rule for the data to be analyzed.

7. The method according to claim 1, characterized in that Calculating a risk value based on target tag information corresponding to the data to be analyzed, and determining whether the data to be analyzed has a compliance risk based on the risk value, includes: Calculate the risk value based on the target sensitivity label corresponding to the data to be analyzed and a preset risk calculation formula; If the risk value exceeds the preset risk threshold, the data to be analyzed is sent to a processing personnel, and based on the processing personnel's confirmation operation on the data to be analyzed, it is determined whether the data to be analyzed has compliance risks; If the risk value does not exceed the preset risk threshold, it is determined that there is no compliance risk in the data to be analyzed.

8. The method according to claim 1, characterized in that The method further comprises: The data to be analyzed that has compliance risks is sent to the processing personnel, and the data to be analyzed is repaired or isolated according to the processing operations of the processing personnel on the data to be analyzed.

9. The method according to claim 8, characterized in that The rule base also includes a first correction rule corresponding to each risk rule; performing data repair or data isolation on the data to be analyzed based on the processing operation of the processing personnel on the data to be analyzed includes: Obtaining the processing operation corresponding to the data to be analyzed by the processing personnel; If the processing operation is to perform data repair on the data to be analyzed, then if there is a corresponding target risk rule for the data to be analyzed, the data to be analyzed is corrected according to a first correction rule corresponding to the target risk rule; and if there is no corresponding target risk rule for the data to be analyzed, the data to be analyzed is corrected according to a second correction rule sent by the processing personnel; If the processing operation is to perform data isolation on the data to be analyzed, an isolation operation is performed on the data to be analyzed according to the data type of the data to be analyzed.

10. The method according to claim 9, characterized in that After correcting the data to be analyzed, the method further includes: Performing repair verification on the data to be analyzed according to the first correction rule or the second correction rule corresponding to the data to be analyzed; In the case that the repair verification of the data to be analyzed fails, data restoration is performed on the repaired data to be analyzed, and the repair failure reason corresponding to the data to be analyzed is recorded.

11. A data compliance determination device, characterized in that: Applied to an electronic device, the electronic device stores a label library and a rule library, the label library includes multiple label information, the label information represents the attribute information of the data to be analyzed, and the rule library includes multiple risk rules, the device includes: An acquisition module, configured to acquire a plurality of data to be analyzed and extract metadata corresponding to each of the data to be analyzed; a matching module, configured to match label information of each of the data to be analyzed according to the label library and each of the metadata, and to match risk rules of each of the data to be analyzed according to the label matching results corresponding to each of the data to be analyzed, the metadata corresponding to each of the data to be analyzed, and the rule library; A determination module is configured to determine whether the data to be analyzed has a compliance risk if the data to be analyzed has corresponding target label information and target risk rules; if the data to be analyzed has corresponding target label information but does not have corresponding target risk rules, calculate a risk value based on the target label information corresponding to the data to be analyzed, and determine whether the data to be analyzed has a compliance risk based on the risk value; if the data to be analyzed does not have corresponding target label information but has corresponding target risk rules, send the data to be analyzed to a processing personnel, and determine whether the data to be analyzed has a compliance risk based on the processing personnel's confirmation operation on the data to be analyzed; if the data to be analyzed does not have corresponding target label information and does not have corresponding target risk rules, determine that the data to be analyzed does not have a compliance risk.

12. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the method according to any one of claims 1 to 10.

13. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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