Data governance security solution based on artificial intelligence

Abstract

The invention discloses a data governance security solution method based on artificial intelligence, and relates to the technical field of data management, and the method comprises the following steps: S1, data asset list and classification; s2, data risk assessment; s3, making a data access control strategy; s4, performing data encryption and desensitization processing; s5, data transmission security management; s6, performing data storage security management; s7, performing data use and sharing security management; s8, monitoring and responding to a data security event; and S9, performing data governance security compliance auditing. According to the data governance security solution based on artificial intelligence, by introducing an AI-driven dynamic label management mechanism, a self-adaptive transmission protocol selection mechanism and a dynamic authorization mechanism based on behavior analysis, real-time adjustment and fine management of a data security policy are realized, and potential threats can be quickly responded and prevented; therefore, the security, flexibility and efficiency of data management are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and specifically to an artificial intelligence-based data governance security solution. Background Art

[0002] In today's digital age, with the rapid growth of data volume and the increasing complexity of data types, data security management faces unprecedented challenges. Traditional data governance security solutions usually rely on static rules and manually configured security policies, which are difficult to cope with changing security threats and complex data environments. These traditional methods have shortcomings such as delayed response and insufficient protection in dealing with data leaks, unauthorized access, malware attacks, etc., and cannot adjust security policies in real time, and it is difficult to efficiently handle security needs in large-scale data scenarios. The specific shortcomings are as follows:

[0003] Static security policy: Traditional methods rely on pre-set rules and lack the ability to dynamically adjust, making it difficult to cope with real-time changing security threats.

[0004] Delayed response: Existing security monitoring technologies usually respond to security incidents only after they occur and are unable to prevent threats in the early stages.

[0005] Extensive management: Traditional methods cannot perform refined management based on changes in user behavior patterns and data environment, and are prone to problems such as "over-authorization" or "insufficient power".

[0006] Lack of intelligence: Existing technologies rely on manual configuration and management and cannot automatically adapt to complex data environments and security requirements, resulting in low efficiency. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides an artificial intelligence-based data governance security solution to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a data governance security solution based on artificial intelligence, comprising the following steps:

[0009] S1. Data asset inventory and classification

[0010] Establish a data asset inventory, classify the data, and automate the identification and classification of data assets based on the AI ​​system;

[0011] S2. Data Risk Assessment

[0012] The AI ​​system assigns risk scores to each type of data based on the risk assessment model, analyzes existing threats such as data leakage, unauthorized access, and data tampering, and updates the assessment results in real time to dynamically reflect changes in the data environment.

[0013] S3. Data access control strategy formulation

[0014] According to the data risk assessment results, the AI ​​system automatically generates data access control policies based on the dynamic authorization mechanism of behavioral analysis. The policies include user role definition, permission allocation, and access auditing.

[0015] S4. Data encryption and desensitization

[0016] The AI ​​system automatically identifies the data categories that need to be encrypted and desensitized, and selects appropriate encryption algorithms and desensitization technologies for processing;

[0017] S5. Data transmission security management

[0018] The AI ​​system automatically configures transmission encryption protocols, such as SSL / TLS, based on risk assessment of the transmission path. It also has a built-in adaptive transmission protocol selection mechanism that dynamically switches encryption methods based on the network environment and the sensitivity of the transmitted data.

[0019] S6. Data storage security management

[0020] The AI ​​system analyzes the security of storage methods between different data and automatically configures corresponding security policies;

[0021] S7. Data usage and sharing security management

[0022] The AI ​​system adds access control tags to data based on a dynamic tag management mechanism, analyzes the behavior patterns of data users, and automatically adjusts data sharing permissions;

[0023] S8. Data security incident monitoring and response

[0024] The AI ​​system monitors data security events in real time, identifies abnormal behaviors and potential threats, triggers alarms in a timely manner, and automatically executes response measures, including data isolation, permission revocation, and system patching;

[0025] S9. Data governance security compliance audit

[0026] The AI ​​system uses automated auditing tools to analyze whether a company’s data governance complies with relevant laws, regulations, and industry standards.

[0027] To further optimize the technical solution, in step S1, the data asset list includes all structured and unstructured data sources inside and outside the company, such as databases, file storage, and API interfaces;

[0028] The AI ​​system automatically analyzes data content through natural language processing and pattern recognition technology, and classifies data into sensitive data, non-sensitive data, personal data, and financial data; the classification standards are based on the company's security policies, compliance requirements, and data usage scenarios.

[0029] Further optimizing the technical solution, in step S2, the risk assessment model is modeled based on historical security events, current security situation and predicted trends to identify potential weak links in data governance;

[0030] The risk assessment model is as follows:

[0031]

[0032] in,

[0033] ( ) represents the ) category data;

[0034] ( ) is the sensitivity of the data; sensitivity is determined based on the type of data and its potential business impact;

[0035] ( ) is the exposure of data, which indicates the possibility of data being exposed to the external environment in an unencrypted or unprotected state;

[0036] ( ) is the data access frequency, indicating the frequency with which the data is accessed;

[0037] ( ) is the possibility of data leakage, based on the security of the environment where the data is located and the vulnerability of the data itself;

[0038] ( ) is the threat level, representing the severity of the potential threat;

[0039] ( ) is the adjustment parameter of the model, which is used to adjust the influence weight of each factor on the risk score.

[0040] To further optimize the technical solution, in step S3, the dynamic authorization mechanism based on behavior analysis specifically includes the following contents:

[0041] User behavior modeling: The AI ​​system models each user’s daily behavior;

[0042] Behavioral deviation detection: When a user attempts to access data, the AI ​​system analyzes the difference between the current behavior and the behavioral baseline in real time;

[0043] Dynamic permission adjustment: Based on detected behavioral deviations, the AI ​​system will dynamically adjust the user's access rights;

[0044] Continuous learning and adaptability: AI systems are not fixed, but dynamically adjust the behavior baseline by continuously learning user behavior patterns;

[0045] Combination of behavioral analysis and risk scoring: The AI ​​system determines the dynamic adjustment of access rights based on the risk score of user behavior.

[0046] To further optimize the technical solution, in step S4, the selection of the encryption algorithm is based on the data type, sensitivity and usage scenario, such as using the Advanced Encryption Standard (AES) for personal information and homomorphic encryption for financial data;

[0047] Desensitization processing uses AI to automatically generate desensitization rules to maintain the anonymity of data during circulation.

[0048] To further optimize the technical solution, in step S5, the adaptive transmission protocol selection mechanism uses a dynamic selection formula to evaluate and select the optimal transmission protocol in real time according to multiple factors. The transmission protocol selection formula is as follows:

[0049]

[0050] in,

[0051] ( ) means the first ) transport protocols, ( ) indicates the total number of transport protocols available in the system;

[0052] ( ) is the data sensitivity, which is assessed based on the type of data and importance;

[0053] ( ) is the encryption strength requirement, which is determined based on the risk assessment results of the transmitted data;

[0054] ( ) is the network condition, which is evaluated based on the bandwidth, delay and jitter of the current network;

[0055] ( ) is the bandwidth requirement, taking into account the size and urgency of the data being transmitted;

[0056] ( ) is the transmission protocol ( )’s computational overhead;

[0057] ( ) is the weight parameter of the model, which is used to balance the influence of different factors;

[0058] ( ) is a small positive number used to avoid the denominator being zero.

[0059] To further optimize the technical solution, in step S6, the storage method includes cloud storage, local storage, and distributed storage; the security strategy includes data sharding storage, redundant backup, and access log recording;

[0060] At the same time, the AI ​​system uses predictive analysis to identify potential risks in the storage system, including insufficient storage capacity, hardware failure, and data corruption, and provides corresponding response measures.

[0061] To further optimize the technical solution, in step S7, the dynamic tag management mechanism includes the following contents:

[0062] Intelligent label assignment: The AI ​​system assigns an initial label to each data object, and the label defines the access control policy of the data;

[0063] Behavioral pattern analysis: The AI ​​system continuously monitors the behavior of data users and builds behavioral models;

[0064] Dynamic label adjustment: When the behavior of data users or the data sharing environment changes, the AI ​​system dynamically adjusts data labels based on real-time monitoring of behavior patterns and environmental information;

[0065] Abnormal behavior monitoring and response: The AI ​​system dynamically adjusts data labels and monitors abnormal behaviors in data use and sharing in real time;

[0066] Label adaptability and continuous optimization: The AI ​​system dynamically adjusts label content and allocation strategies by optimizing behavior patterns.

[0067] To further optimize the technical solution, in step S8, abnormal behaviors and potential threats are identified based on a recognition model, and the recognition model is as follows:

[0068]

[0069] in,

[0070] ( ) is the risk score, which represents the possibility and severity of the detected event being judged as a security threat;

[0071] ( ) is the activity intensity, which refers to the first ( ) The intensity of data activities;

[0072] ( ) is the potential impact, that is, ) The impact on the system when such activities are judged as security threats;

[0073] ( ) is the vulnerability coefficient, reflecting the first ( ) The vulnerability of systems or data involved in such activities;

[0074] ( ) is the time factor, which indicates the influence of time on detection;

[0075] ( ) is a weighting factor used to balance the importance of different activity types in the overall risk score.

[0076] ( ) is a constant used to avoid the denominator being zero or other unreasonable situations, and is taken to be a very small positive number.

[0077] To further optimize the technical solution, in step S9, the audit content includes data classification and encryption, access control policies, data usage and sharing records, and generates a detailed compliance report listing compliance risks and improvement suggestions.

[0078] Compared with the prior art, the present invention provides a data governance security solution based on artificial intelligence, which has the following beneficial effects:

[0079] This AI-based data governance security solution, by introducing AI-driven dynamic tag management mechanism, adaptive transmission protocol selection mechanism, and dynamic authorization mechanism based on behavior analysis, achieves real-time adjustment and refined management of data security policies, and can quickly respond to and prevent potential threats, thereby greatly improving the security, flexibility, and efficiency of data governance. It not only improves the company's data protection capabilities, but also ensures the security and compliance of data use and sharing in a complex and changing environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A flowchart of a data governance security solution based on artificial intelligence proposed by the present invention;

[0081] Figure 2 A flowchart of a dynamic authorization mechanism in an artificial intelligence-based data governance security solution proposed by the present invention;

[0082] Figure 3 A selection formula diagram of an adaptive transmission protocol selection mechanism in an artificial intelligence-based data governance security solution proposed by the present invention;

[0083] Figure 4 This is a flow chart of the dynamic tag management mechanism in an artificial intelligence-based data governance security solution proposed by the present invention. DETAILED DESCRIPTION

[0084] 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 described embodiments 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 creative work are within the scope of protection of the present invention.

[0085] Example:

[0086] See also Figure 1 , an artificial intelligence-based data governance security solution, including the following steps:

[0087] S1. Data asset inventory and classification

[0088] Establish a data asset inventory, classify the data, and automate the identification and classification of data assets based on the AI ​​system.

[0089] In this embodiment, the data asset list includes all structured and unstructured data sources inside and outside the company, such as databases, file storage, and API interfaces.

[0090] The AI ​​system automatically analyzes data content through natural language processing and pattern recognition technology, and classifies data into sensitive data, non-sensitive data, personal data, and financial data; the classification standards are based on the company's security policies, compliance requirements, and data usage scenarios.

[0091] S2. Data Risk Assessment

[0092] The AI ​​system scores each type of data based on the risk assessment model and analyzes existing threats, such as data leakage, unauthorized access, and data tampering. The assessment results are updated in real time to dynamically reflect changes in the data environment.

[0093] In this embodiment, in step S2, the risk assessment model is modeled based on historical security events, current security situation and predicted trends to identify potential weaknesses in data governance;

[0094] The risk assessment model is as follows:

[0095]

[0096] in,

[0097] ( ) represents the ) category data;

[0098] ( ) is the sensitivity of the data; sensitivity is determined based on the type of data and its potential business impact; for example, personally identifiable information (PII) and financial data are generally highly sensitive; ( ) value range is ( );

[0099] ( ) is the exposure of the data, which indicates the possibility of data being exposed to the external environment in an unencrypted or unprotected state; the exposure can be evaluated by factors such as historical events and network exposure; ( ) value range is ( );

[0100] ( ) is the data access frequency, which indicates the frequency with which data is accessed; the higher the access frequency, the greater the risk; ( ) is usually a positive integer, indicating the number of visits per unit time;

[0101] ( ) is the possibility of data leakage, based on the security of the data environment (such as firewalls, encryption measures, etc.) and the vulnerability of the data itself; the value of this parameter is predicted by the artificial intelligence system and ranges from ( );

[0102] ( ) is the threat level, which represents the severity of the potential threat; this parameter is measured based on the capabilities, motivations, and opportunities of the threat source; ( ) is in the range of ( );

[0103] ( ) is the adjustment parameter of the model, which is used to adjust the weight of each factor on the risk score; by default, \( ),\( ),\( ),\( ); these parameters can be optimized according to actual business scenarios.

[0104] The AI ​​system first extracts multi-dimensional features from each type of data and generates corresponding parameter values , , , and These parameters are calculated and predicted through machine learning models, which can use, for example, access records, leaks, threat intelligence, etc. in historical data as input. Enterprises can more accurately quantify data risks, identify potential security threats, and develop more effective security strategies.

[0105] S3. Data access control strategy formulation

[0106] According to the results of data risk assessment, the AI ​​system automatically generates data access control policies based on the dynamic authorization mechanism of behavioral analysis. The policies include user role definition, permission allocation, and access auditing.

[0107] like Figure 2 As shown, in this embodiment, in step S3, the dynamic authorization mechanism based on behavior analysis specifically includes the following contents:

[0108] User behavior modeling: The AI ​​system models the daily behavior of each user, including access time, access frequency, access data type, device used, and network location. Through machine learning algorithms, the AI ​​system can identify the behavior pattern of each user and establish a behavior baseline. This baseline represents the typical behavior of the user under normal circumstances.

[0109] Behavioral deviation detection: When a user attempts to access data, the AI ​​system analyzes the difference between the current behavior and the behavioral baseline in real time; this difference is called "behavioral deviation." If the deviation exceeds the preset threshold, such as the user accessing sensitive data at an unusual time, using an unauthorized device to access, or accessing data beyond their normal permissions, the AI ​​system will automatically trigger an alarm and adjust permissions.

[0110] Dynamic permission adjustment: Based on the detected behavioral deviations, the AI ​​system will dynamically adjust the user's access rights; for example, if the user's behavioral deviation is large, the system may temporarily reduce their access rights and only allow them to access basic data; or require them to perform additional identity verification (such as multi-factor authentication). This permission adjustment is real-time, ensuring that users can only access sensitive data when their behavior is confirmed to be normal.

[0111] Continuous learning and adaptability: AI systems are not fixed, but continuously learn user behavior patterns and dynamically adjust the behavior baseline; over time, the system will gradually optimize its understanding of user behavior and reduce false positives and false negatives. For example, if a user's job responsibilities change, the system will automatically update their behavior baseline to adapt to the new access pattern.

[0112] Combining behavioral analysis with risk scoring: The AI ​​system dynamically adjusts access rights based on the risk score of user behavior. For example, if a user's behavior exhibits high-risk characteristics (such as frequent attempts to access unauthorized data), the system may completely revoke their access rights until their behavior is further reviewed.

[0113] Suppose an employee in a company's finance department accesses the company's financial data during normal office hours on a weekday. This behavior will be recorded as the employee's "behavioral baseline." However, if the employee attempts to access the same financial data through an external network during non-working hours, the AI ​​system will identify this abnormal behavior and trigger a behavioral deviation detection mechanism. Depending on the size of the behavioral deviation, the system may require the employee to undergo multi-factor authentication or temporarily restrict their access rights. At the same time, the system will continue to monitor the employee's behavior and update the behavioral baseline to adapt to their possible new working mode.

[0114] S4. Data encryption and desensitization

[0115] The AI ​​system automatically identifies data categories that need to be encrypted and desensitized, and selects appropriate encryption algorithms and desensitizing technologies for processing.

[0116] In this embodiment, the encryption algorithm is selected based on the data type, sensitivity, and usage scenario, such as using the Advanced Encryption Standard (AES) for personal information and homomorphic encryption for financial data;

[0117] Desensitization processing uses AI to automatically generate desensitization rules to maintain the anonymity of data during circulation.

[0118] S5. Data transmission security management

[0119] The AI ​​system automatically configures transmission encryption protocols, such as SSL / TLS protocols, based on risk assessment of the transmission path, and has a built-in adaptive transmission protocol selection mechanism that dynamically switches encryption methods according to the network environment and the sensitivity of the transmitted data.

[0120] like Figure 3 As shown, in this embodiment, in step S5, the adaptive transmission protocol selection mechanism uses a dynamic selection formula to evaluate and select the optimal transmission protocol in real time according to multiple factors. The transmission protocol selection formula is as follows:

[0121]

[0122] in,

[0123] ( ) indicates the first ) transport protocols (such as SSL, TLS, IPSec, etc.), ( ) indicates the total number of transport protocols available in the system;

[0124] ( ) is the data sensitivity, which is assessed based on the type of data and importance; sensitive data (such as financial information, personal identity information) requires a higher level of encryption protocol; ( ) is the value of ( )

[0125] ( ) is the encryption strength requirement, which is determined based on the risk assessment results of the transmitted data; higher encryption strength is usually accompanied by greater computational overhead; ( ) is the value of ( )

[0126] ( ) is the network condition, which is evaluated based on the bandwidth, latency, and jitter of the current network; good network conditions allow for higher encryption standards without significantly affecting transmission speed; ( ) is the value of ( )

[0127] ( ) is the bandwidth requirement, considering the size and urgency of the data being transmitted; data that needs to be transmitted quickly may prefer protocols that occupy less bandwidth; ( ) is the value of ( )

[0128] ( ) is the transmission protocol ( ) computational overhead. Usually, protocols with higher security levels have greater computational complexity and higher latency.

[0129] ( ) is the weight parameter of the model, which is used to balance the influence of different factors. The weight is adjusted according to business needs;

[0130] ( ) is a small positive number used to avoid the denominator being zero.

[0131] During data transmission, the AI ​​system will dynamically calculate the scores of each transmission protocol based on the real-time changes in data characteristics and network environment ( ), and selects the protocol with the highest score for data transmission.

[0132] During the transmission process, if the network conditions deteriorate or the encryption requirements change, the AI ​​system will re-evaluate the scores of each protocol and may dynamically switch to another protocol that is more suitable for the current situation to ensure the efficiency and security of data transmission.

[0133] S6. Data storage security management

[0134] The AI ​​system analyzes the security of storage methods between different data and automatically configures corresponding security policies.

[0135] In this embodiment, the storage methods include cloud storage, local storage, and distributed storage; the security policies include data sharding storage, redundant backup, and access log recording;

[0136] At the same time, the AI ​​system uses predictive analysis to identify potential risks in the storage system, including insufficient storage capacity, hardware failure, and data corruption, and provides corresponding response measures.

[0137] S7. Data usage and sharing security management

[0138] The AI ​​system adds access control tags to data based on a dynamic tag management mechanism, analyzes the behavior patterns of data users, and automatically adjusts data sharing permissions.

[0139] like Figure 4 As shown, in this embodiment, the dynamic tag management mechanism includes the following contents:

[0140] Intelligent label assignment: The AI ​​system first assigns initial labels to each data object. These labels define the access control policy of the data. The label content includes the sensitivity level of the data, the accessible user group, the usage restrictions, the sharing restrictions, etc. The initial label assignment is based on the classification of the data, risk assessment and business needs.

[0141] Behavioral pattern analysis: AI systems continuously monitor the behavior of data users and build behavioral models. These models reflect the normal behavior patterns of users in specific environments, including access frequency, access time, access data types, shared objects, etc. When the behavior of data users changes (such as access or sharing behaviors that exceed normal usage patterns), the AI ​​system will automatically adjust the labels of data objects to adapt to new usage scenarios.

[0142] Dynamic label adjustment: When the behavior of data users or the data sharing environment changes, the AI ​​system will dynamically adjust the data labels based on the real-time monitoring of behavioral patterns and environmental information. For example, when a user is detected trying to access sensitive data, the AI ​​system may automatically increase the sensitivity level of the data and add additional access control requirements; or when sharing data, the system will dynamically adjust the sharing conditions according to the permissions and environment of the shared object to ensure data security.

[0143] Abnormal behavior monitoring and response: AI systems can not only dynamically adjust data labels, but also have the ability to monitor abnormal behavior in data use and sharing in real time. For example, when the system detects frequent access failures or unauthorized data requests, it may trigger a label upgrade to mark the data as "highly sensitive" or "internal use only", thereby immediately tightening access and sharing permissions. The system can also take immediate action, such as suspending sharing, notifying security administrators, or requiring additional authentication.

[0144] Label adaptability and continuous optimization: The AI ​​system can dynamically adjust the content and allocation strategy of labels by continuously learning and optimizing behavior patterns. The system can identify which label settings are more effective, thereby better protecting data in subsequent security policy applications. In addition, the AI ​​system can automatically generate new label types based on emerging security threats and business needs to adapt to the ever-changing environment.

[0145] S8. Data security incident monitoring and response

[0146] The AI ​​system monitors data security incidents in real time, identifies abnormal behavior and potential threats, triggers alarms in a timely manner, and automatically executes response measures, including data isolation, permission revocation, and system patching.

[0147] In this embodiment, in step S8, abnormal behaviors and potential threats are identified based on a recognition model, and the recognition model is as follows:

[0148]

[0149] in,

[0150] ( ) is the risk score, which represents the possibility and severity of the detected event being judged as a security threat;

[0151] ( ) is the activity intensity, which refers to the first ( ) The intensity of data activities; for example, the frequency of user access, the amount of data transmission, etc.;

[0152] ( ) is the potential impact, that is, ) activities are identified as security threats, the impact that may be caused to the system; illegal access to sensitive data has a higher potential impact;

[0153] ( ) is the vulnerability coefficient, reflecting the first ( ) The vulnerability of the system or data involved in such activities; for example, a system with known vulnerabilities will have a higher vulnerability coefficient;

[0154] ( ) is the time factor, which indicates the effect of time on detection; for example, the risk of some threats increases over time, so time needs to be weighted; ( ) represents the window period of detection time. The larger the value, the higher the risk may be.

[0155] ( ) is a weight factor used to balance the importance of different activity types in the overall risk score; based on business needs and data characteristics, the weight factor can be adjusted to more accurately reflect the actual risk;

[0156] ( ) is a constant used to avoid the denominator being zero or other unreasonable situations, and is taken to be a very small positive number.

[0157] In practical applications, the recognition model can detect and respond to data security incidents through the following steps:

[0158] The AI ​​system first collects all data activities in the current system, including user login, file access, data transfer, etc. ) and vulnerability ( ) is evaluated and the model can preliminarily identify activities that may be risky.

[0159] The possible impact of AI systems on each activity ( ), especially when the activity involves sensitive data or critical systems, the model will increase the impact coefficient of the activity, indicating that the potential risk is greater.

[0160] The model incorporates a time factor ( ), calculate the risk score for the current period ( ). For some possible security events, such as unauthorized access attempts or unusual network traffic, the risk score may gradually increase over time.

[0161] The AI ​​system continuously calculates risk scores for various activities and compares them with preset thresholds. Once the risk score of an activity is ) exceeds the threshold, the system will immediately trigger an alarm and notify the security team.

[0162] Based on the risk score, the AI ​​system automatically executes a series of response measures. For example, for high-risk events, the system may immediately isolate the affected data or revoke user permissions to avoid further damage. At the same time, the AI ​​system will also automatically patch, close known vulnerabilities or strengthen protection measures to prevent similar incidents from happening again.

[0163] S9. Data governance security compliance audit

[0164] The AI ​​system uses automated auditing tools to analyze whether a company’s data governance complies with relevant laws, regulations, and industry standards.

[0165] In this embodiment, the audit content includes data classification and encryption, access control policies, data usage and sharing records, and generates a detailed compliance report that lists compliance risks and improvement suggestions.

[0166] The beneficial effects of the present invention are:

[0167] This AI-based data governance security solution, by introducing AI-driven dynamic tag management mechanism, adaptive transmission protocol selection mechanism, and dynamic authorization mechanism based on behavior analysis, achieves real-time adjustment and refined management of data security policies, and can quickly respond to and prevent potential threats, thereby greatly improving the security, flexibility, and efficiency of data governance. It not only improves the company's data protection capabilities, but also ensures the security and compliance of data use and sharing in a complex and changing environment.

[0168] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0169] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data governance security solution based on artificial intelligence, characterized in that: The following steps are involved: S1. Data asset inventory and classification Establish a data asset inventory, classify the data, and automate the identification and classification of data assets based on the AI ​​system; S2. Data Risk Assessment The AI ​​system assigns risk scores to each type of data based on the risk assessment model, analyzes existing threats such as data leakage, unauthorized access, and data tampering, and updates the assessment results in real time to dynamically reflect changes in the data environment. S3. Data access control strategy formulation According to the data risk assessment results, the AI ​​system automatically generates data access control policies based on the dynamic authorization mechanism of behavioral analysis. The policies include user role definition, permission allocation, and access auditing. S4. Data encryption and desensitization The AI ​​system automatically identifies the data categories that need to be encrypted and desensitized, and selects appropriate encryption algorithms and desensitization technologies for processing; S5. Data transmission security management The AI ​​system automatically configures transmission encryption protocols, such as SSL / TLS, based on risk assessment of the transmission path. It also has a built-in adaptive transmission protocol selection mechanism that dynamically switches encryption methods based on the network environment and the sensitivity of the transmitted data. S6. Data storage security management The AI ​​system analyzes the security of storage methods between different data and automatically configures corresponding security policies; S7. Data usage and sharing security management The AI ​​system adds access control tags to data based on a dynamic tag management mechanism, analyzes the behavior patterns of data users, and automatically adjusts data sharing permissions; S8. Data security incident monitoring and response The AI ​​system monitors data security events in real time, identifies abnormal behaviors and potential threats, triggers alarms in a timely manner, and automatically executes response measures, including data isolation, permission revocation, and system patching; S9. Data governance security compliance audit The AI ​​system uses automated auditing tools to analyze whether a company’s data governance complies with relevant laws, regulations, and industry standards.

2. According to claim 1, a data governance security solution based on artificial intelligence is characterized in that: In step S1, the data asset list includes all structured and unstructured data sources inside and outside the company, such as databases, file storage, and API interfaces; The AI ​​system automatically analyzes data content through natural language processing and pattern recognition technology, and classifies data into sensitive data, non-sensitive data, personal data, and financial data; the classification standards are based on the company's security policies, compliance requirements, and data usage scenarios.

3. According to claim 1, a data governance security solution based on artificial intelligence is characterized in that: In step S2, the risk assessment model is modeled based on historical security events, current security situation and predicted trends to identify potential weaknesses in data governance; The risk assessment model is as follows: ； in, ( ) represents the ) category data; ( ) is the sensitivity of the data; sensitivity is determined based on the type of data and its potential business impact; ( ) is the exposure of data, which indicates the possibility of data being exposed to the external environment in an unencrypted or unprotected state; ( ) is the data access frequency, indicating the frequency with which the data is accessed; ( ) is the possibility of data leakage, based on the security of the environment where the data is located and the vulnerability of the data itself; ( ) is the threat level, representing the severity of the potential threat; ( ) is the adjustment parameter of the model, which is used to adjust the influence weight of each factor on the risk score.

4. According to claim 1, a data governance security solution based on artificial intelligence is characterized in that: In step S3, the dynamic authorization mechanism based on behavior analysis specifically includes the following contents: User behavior modeling: The AI ​​system models each user’s daily behavior; Behavioral deviation detection: When a user attempts to access data, the AI ​​system analyzes the difference between the current behavior and the behavioral baseline in real time; Dynamic permission adjustment: Based on detected behavioral deviations, the AI ​​system will dynamically adjust the user's access rights; Continuous learning and adaptability: AI systems are not fixed, but dynamically adjust the behavior baseline by continuously learning user behavior patterns; Combination of behavioral analysis and risk scoring: The AI ​​system determines the dynamic adjustment of access rights based on the risk score of user behavior.

5. According to claim 1, a data governance security solution based on artificial intelligence is characterized in that: In step S4, the encryption algorithm is selected based on the data type, sensitivity and usage scenario, such as using the Advanced Encryption Standard (AES) for personal information and homomorphic encryption for financial data; Desensitization processing uses AI to automatically generate desensitization rules to maintain the anonymity of data during circulation.

6. The data governance security solution based on artificial intelligence according to claim 1 is characterized in that: In step S5, the adaptive transmission protocol selection mechanism uses a dynamic selection formula to evaluate and select the optimal transmission protocol in real time based on multiple factors. The transmission protocol selection formula is as follows: ； in, ( ) means the first ) transport protocols, ( ) indicates the total number of transport protocols available in the system; ( ) is the data sensitivity, which is assessed based on the type of data and importance; ( ) is the encryption strength requirement, which is determined based on the risk assessment results of the transmitted data; ( ) is the network condition, which is evaluated based on the bandwidth, delay and jitter of the current network; ( ) is the bandwidth requirement, taking into account the size and urgency of the data being transmitted; ( ) is the transmission protocol ( )’s computational overhead; ( ) is the weight parameter of the model, which is used to balance the influence of different factors; ( ) is a small positive number used to avoid the denominator being zero.

7. The data governance security solution based on artificial intelligence according to claim 1 is characterized in that: In step S6, the storage method includes cloud storage, local storage, and distributed storage; the security strategy includes data sharding storage, redundant backup, and access log recording; At the same time, the AI ​​system uses predictive analysis to identify potential risks in the storage system, including insufficient storage capacity, hardware failure, and data corruption, and provides corresponding response measures.

8. The data governance security solution based on artificial intelligence according to claim 1 is characterized in that: In step S7, the dynamic tag management mechanism includes the following contents: Intelligent label assignment: The AI ​​system assigns an initial label to each data object, and the label defines the access control policy of the data; Behavioral pattern analysis: The AI ​​system continuously monitors the behavior of data users and builds behavioral models; Dynamic label adjustment: When the behavior of data users or the data sharing environment changes, the AI ​​system dynamically adjusts data labels based on real-time monitoring of behavior patterns and environmental information; Abnormal behavior monitoring and response: The AI ​​system dynamically adjusts data labels and monitors abnormal behaviors in data use and sharing in real time; Label adaptability and continuous optimization: The AI ​​system dynamically adjusts label content and allocation strategies by optimizing behavior patterns.

9. The data governance security solution based on artificial intelligence according to claim 1 is characterized in that: In step S8, abnormal behaviors and potential threats are identified based on a recognition model, and the recognition model is as follows: ； in, ( ) is the risk score, which represents the possibility and severity of the detected event being judged as a security threat; ( ) is the activity intensity, which refers to the first ( ) The intensity of data activities; ( ) is the potential impact, that is, ) The impact on the system when such activities are judged as security threats; ( ) is the vulnerability coefficient, reflecting the first ( ) The vulnerability of systems or data involved in such activities; ( ) is the time factor, which indicates the influence of time on detection; ( ) is a weighting factor used to balance the importance of different activity types in the overall risk score; ( ) is a constant used to avoid the denominator being zero or other unreasonable situations, and is taken to be a very small positive number.

10. The data governance security solution based on artificial intelligence according to claim 1 is characterized in that: In step S9, the audit content includes data classification and encryption, access control policies, data usage and sharing records, and generates a detailed compliance report listing compliance risks and improvement suggestions.

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