Network data security risk identification and evaluation method

Abstract

The invention discloses a network data security risk identification and evaluation method, and relates to the technical field of data security risks, and the network data security risk identification and evaluation method comprises the following steps: S1, data security risk factors; s2, data security requirements; s3, the possibility of data security risk occurrence is determined; s4, a data security risk identification method based on multiple elements; and S5, a data security risk assessment method. According to the network data security risk identification and evaluation method, implementation thoughts and key points can be provided for data security risk evaluation and even risk prevention and control, on one hand, the method is beneficial to further defining the range of risk evaluation and promoting analysis of a complex service scene and a life cycle link of the complex service scene; and on the other hand, the method clarifies dimensions and specific element contents which need to be identified and evaluated, and can effectively clarify the target of risk evaluation and analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of data security risks, and in particular to a method for identifying and evaluating network data security risks. Background Art

[0002] Data security risks include but are not limited to data leakage, data tampering, data abuse, illegal transmission, illegal access, traffic anomalies, etc. In recent years, the digital transformation of global enterprises has accelerated, and data leakage and data destruction have continued to grow, seriously affecting social production and threatening the personal rights and interests of citizens. Data security risk prevention is urgent. Faced with the increasingly prominent data security issues in the process of digital transformation of enterprises, it is necessary to build an enterprise data security risk identification and assessment plan, identify data security risk factors, and enhance data security risk identification capabilities.

[0003] Therefore, in view of this, the existing structure and deficiencies are studied and improved, and a method for identifying and assessing network data security risks is proposed. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for identifying and evaluating network data security risks, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: The network data security risk identification and assessment method comprises the following steps: S1. Data security risk factors: Starting from the perspectives of the data security risk's impact object, inducing source, and asset exposure, the common data security risk factors are extracted and the correlation between risk factors is established. Data security risk factors include data, business, data processing activities, data carriers, threats, vulnerabilities, and existing security measures. S2. Data security requirements: The security requirements of data include confidentiality requirements, integrity requirements, availability requirements, controllability requirements, and compliance requirements. In the process of identifying and evaluating the impact of data security risks, on the one hand, it is necessary to judge the value of the data itself, and on the other hand, it is necessary to analyze the security requirements of the data. Specifically, the analysis is conducted by collecting information on the vulnerabilities faced by data in collection, storage, transmission, and processing activities, as well as the existing security measures. S3. Possibility of data security risks: Threats can cause potential damage to data and are an objective risk factor. By assigning values ​​to the motivation, ability, and frequency of occurrence of threats and combining vulnerabilities with existing security measures, we can analyze the possibility of risk occurrence. In the process of identifying and evaluating the possibility of data security risks, we need to analyze the possibility of threats based on the exploitability of vulnerabilities and existing security measures. The exploitability of vulnerabilities should be summarized and recorded based on existing past experience. Since different stages of the data lifecycle have inherent vulnerabilities, it is necessary to comprehensively analyze the inherent security flaws of the data lifecycle based on specific lifecycle stages; S4. Data security risk identification method based on multiple factors: Establish a risk identification model, which includes data security risks, data security risk factors and evaluation dimensions. Data security risks include impact and possibility of occurrence. The impact corresponds to data value, data security requirements and existing security measures under the data security risk factor unit. The possibility of occurrence corresponds to threats and inherent vulnerabilities in the entire life cycle under the data security risk factor unit. Data value corresponds to authenticity, consistency, and integrity under the evaluation dimension unit, and data security requirements correspond to confidentiality requirements, integrity requirements, availability requirements, controllability requirements, and compliance requirements under the evaluation dimension unit; The threat corresponds to the source, motivation, and capability under the evaluation latitude unit, and the inherent vulnerability of the whole life cycle corresponds to the scenario, equipment, and personnel under the evaluation latitude unit; S5. Data security risk assessment method: The risk identification model is used to identify the unit content under the evaluation dimension corresponding to the risk factor to determine the data security risk level. The higher the unit content judgment pass degree, the lower the data security risk level; The failed unit content is a risk, and the risk is evaluated based on the impact consequences corresponding to the risk content.

[0006] Furthermore, in step S1, the data elements are explained as follows: data has value, which directly affects the degree of impact of data security risks. The value is generated based on consumption and use. The more consumption and use, the greater the value; The business elements are as follows: Business relies on data and data processing activities to realize business value. The degree of impact that directly affects data security risks; The elements of data processing activities are explained as follows: data processors collect, store, use, process, transmit, provide, and disclose data in order to realize business value. Data processing activities should comply with the requirements of data security laws and regulations. The threats and vulnerabilities faced by each activity will directly determine the possibility of data security risks.

[0007] Furthermore, in step S1, the data carrier element is described as follows: data processors carry out data processing activities based on data carriers, which are vulnerable and always face threats, and their security status will directly affect the possibility of data security risks; The threat factors are explained as follows: As an objective risk source, the source, capability and frequency of threat will affect the probability of data security risk occurring.

[0008] Furthermore, in step S1, the vulnerability elements are described as follows: the vulnerability can be exploited by threats, the harm induced by the exploitation will affect the degree of impact of the data security risk, and the cost of the exploitation will affect the probability of the data security risk.

[0009] Furthermore, in step S1, the existing security measures elements are described as follows: the existing security measures serve data security protection and will affect the degree of impact of data security risks induced by vulnerabilities.

[0010] Furthermore, in step S2, the confidentiality requirements are as follows: confidentiality protection of data throughout the entire life cycle, protection of sensitive data, and protection of keys throughout the entire life cycle; The integrity requirements are as follows: ensure that the data source is verifiable, data transmission and storage are complete, data calculation is reliable, and data is auditable.

[0011] Furthermore, in step S2, the availability requirements are described as follows: data carrier availability, security analysis capability, original data consistency, and backup mechanism completeness.

[0012] Furthermore, in step S2, the controllability requirements are as follows: data flow value and ownership protection across organizations and borders; The compliance requirements are described as follows: compliance with national laws, regulations, standards, etc., possible social and public safety impacts, and potential cultural inclusion issues.

[0013] Furthermore, in step S3, inherent security defects include, but are not limited to, whether the scope of data collection is effectively controlled during the data collection process, as well as issues such as the quality and legality of the collected content.

[0014] Furthermore, in step S4, the authenticity, consistency, and integrity of the data, on the one hand, determine the value of the data itself, and on the other hand, affect the manifestation of data security risks in specific business scenarios.

[0015] The present invention provides a method for identifying and assessing network data security risks, which has the following beneficial effects: 1. This network data security risk identification and assessment method can provide implementation ideas and key points for data security risk assessment and even risk prevention and control. On the one hand, this method helps to further clarify the scope of risk assessment and promote the analysis of complex business scenarios and their life cycle links; on the other hand, this method clarifies the dimensions and specific elements that need to be identified and assessed, and can effectively clarify the goals of risk assessment and analysis, carry out assessment work from the perspectives of data asset identification, legal and regulatory compliance, data processing activities, data flow, data carriers or supporting environments, create visual business flows and data flows, promote the construction of clear assessment and analysis logic, and identify data security risks and determine whether the risk level is acceptable by establishing a risk factor analysis matrix. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic table of data security risk factors of the present invention; Figure 2 This is a schematic diagram of the data security requirements analysis of the present invention; Figure 3 This is a schematic diagram of the data security risk identification method based on multiple factors of the present invention. DETAILED DESCRIPTION

[0017] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0018] like Figure 1-Figure 3 As shown, the present invention provides a technical solution: the network data security risk identification and assessment method comprises the following steps: S1. Data security risk factors: Starting from the perspectives of the data security risk's impact object, inducing source, and asset exposure, the common data security risk factors are extracted and the correlation between risk factors is established. Data security risk factors include data, business, data processing activities, data carriers, threats, vulnerabilities, and existing security measures. The data elements are explained as follows: data has value, which directly affects the degree of impact of data security risks. The value is generated based on consumption and use. The more consumption and use, the greater the value; The business elements are as follows: Business relies on data and data processing activities to realize business value. The degree of impact that directly affects data security risks; The elements of data processing activities are explained as follows: data processors collect, store, use, process, transmit, provide and disclose data to realize business value. Data processing activities should comply with data security laws and regulations. The threats and vulnerabilities faced by each activity will directly determine the possibility of data security risks. The data carrier elements are explained as follows: Data processors carry out data processing activities based on data carriers, which are vulnerable and face threats at all times. Their security status will directly affect the possibility of data security risks occurring; The threat factors are explained as follows: As an objective risk source, the source, capability and frequency of threats will affect the probability of data security risks occurring; The vulnerability elements are explained as follows: Vulnerabilities can be exploited by threats. The harm caused by the exploitation of threats will affect the degree of impact of data security risks. The cost of the exploitation of threats will affect the probability of data security risks occurring. The elements of existing security measures are explained as follows: Existing security measures serve data security protection and will affect the degree of impact of data security risks induced by vulnerabilities; S2. Data security requirements: The security requirements of data include confidentiality requirements, integrity requirements, availability requirements, controllability requirements, and compliance requirements. In the process of identifying and evaluating the impact of data security risks, on the one hand, it is necessary to judge the value of the data itself, and on the other hand, it is necessary to analyze the security requirements of the data. Specifically, the analysis is conducted by collecting information on the vulnerabilities faced by data in collection, storage, transmission, and processing activities, as well as the existing security measures. The confidentiality requirements are as follows: confidentiality protection of data throughout the entire life cycle, protection of sensitive data, and protection of keys throughout the entire life cycle; The integrity requirements are as follows: ensure that the data source is verifiable, data transmission and storage are complete, data calculation is reliable, and data is auditable; The availability requirements are as follows: data carrier availability, security analysis capability, original data consistency, and backup mechanism completeness; The controllability requirements are as follows: the value of data flow across organizations and borders, and the protection of ownership; The compliance requirements are as follows: compliance with national laws, regulations, standards, etc., possible social and public safety impacts, and potential cultural inclusion issues; S3. Possibility of data security risks: Threats can cause potential damage to data and are an objective risk factor. By assigning values ​​to the motivation, ability, and frequency of occurrence of threats and combining vulnerabilities with existing security measures, we can analyze the possibility of risk occurrence. In the process of identifying and evaluating the possibility of data security risks, we need to analyze the possibility of threats based on the exploitability of vulnerabilities and existing security measures. The exploitability of vulnerabilities should be summarized and recorded based on existing past experience. Since different stages of the data lifecycle have inherent vulnerabilities, it is necessary to comprehensively analyze the inherent security flaws of the data lifecycle based on specific lifecycle stages; Among them, inherent security defects include but are not limited to whether the scope of data collection is effectively controlled during the data collection process, as well as issues such as the quality and legality of the collected content; S4. Data security risk identification method based on multiple factors: Establish a risk identification model, which includes data security risks, data security risk factors and evaluation dimensions. Data security risks include impact and possibility of occurrence. The impact corresponds to data value, data security requirements and existing security measures under the data security risk factor unit. The possibility of occurrence corresponds to threats and inherent vulnerabilities in the entire life cycle under the data security risk factor unit. Data value corresponds to authenticity, consistency, and integrity under the evaluation dimension unit, and data security requirements correspond to confidentiality requirements, integrity requirements, availability requirements, controllability requirements, and compliance requirements under the evaluation dimension unit; The threat corresponds to the source, motivation, and capability under the evaluation latitude unit, and the inherent vulnerability of the whole life cycle corresponds to the scenario, equipment, and personnel under the evaluation latitude unit; The authenticity, consistency, and integrity of data determine the value of the data itself, and affect the manifestation of data security risks in specific business scenarios. S5. Data security risk assessment method: The risk identification model is used to identify the unit content under the evaluation dimension corresponding to the risk factor to determine the data security risk level. The higher the unit content judgment pass degree, the lower the data security risk level; The failed unit content is the risk, and the risk is evaluated based on the impact consequences corresponding to the risk content. The economic loss caused by the risk is the impact consequence, that is, the degree of impact. The higher the economic loss, the greater the risk. Example

[0019] Take the credit application process of the credit business as an example. This business is one of the most typical business scenarios for institutions such as banks that provide loan services. The operation process of business personnel involves specific actions such as checking the credit limit, identity authentication, document uploading, real-name authentication, liveness recognition, information collection, electronic signature, and application submission. The process covers data collection, transmission, and storage. Among them, due to business necessities, high-value personal financial information needs to be stored in the bank's database once collected, facing security risks such as being exported and illegally stolen by internal or unauthorized personnel. In order to effectively assess the data security risks faced by personal financial information data in this scenario, data security risk factors are identified according to the multi-factor data security risk identification method; In the credit business scenario, once the authenticity, integrity and consistency of the data are damaged, it means that there are security defects in the identity authentication link in the credit process, and the collected identity information data is registered by robots. This may cause the institution to face security risks such as data falsification, data unavailability, and unauthorized users modifying data. The data security demand is proportional to the value of the data. The higher the value of the data, the higher its security demand. Taking the personal biometric information used for liveness recognition in the credit scenario as an example, it is sensitive personal information. Once it is leaked or abused, it is easy to cause the personal dignity of natural persons to be infringed or the personal and property safety to be endangered. Therefore, this type of data and its carriers and processing All activities have extremely high confidentiality, integrity, availability, controllability and compliance requirements. This also requires organizations to pay attention not only to the stability of business, business systems and other carriers, but also to the security and compliance of existing technical and management measures in each link for data collection, transmission and storage based on business necessities. Once the status of existing security measures is insufficient to meet their security needs, for example, in the credit granting scenario, business personnel who handle sensitive personal information have not received confidentiality awareness education or have not signed a confidentiality agreement, they may accidentally delete or illegally send out sensitive personal information, resulting in the unavailability of key businesses, infringement of user rights, and the organization facing regulatory compliance penalties and other security risks; The actual impact of data security risks is mainly affected by the above three factors. The higher the value of the data, the higher its security needs. The greater the deviation between the existing security measures and the requirements of its security needs, the greater the impact of data security risks on the organization once they occur. The level of threat and the distribution of vulnerabilities throughout the data life cycle jointly determine the possibility of data security risks. The source, frequency and ability of threats directly constitute the level of threat. The loan credit business involves a large number of users' personal identity information, account information and other sensitive data. The data value is high and faces the threat of being stolen and resold for profit by internal or external parties. Whether it is personnel who can exploit vulnerabilities and defects to launch frequent attacks, or business personnel who are authorized to access and obtain data at any time, they all have the ability to pose advanced threats, which may lead to data security risks such as frequent data leaks and repeated interruptions of business services. Data faces inherent vulnerability issues throughout its entire life cycle. For example, during the transmission stage, data may face vulnerability issues such as interception and theft due to unencrypted transmission of sensitive data, loss of keys, etc. During the storage stage, data may face potential security risks due to vulnerability issues such as plain text storage of sensitive data, unreliable storage media, and unavailable data backups. This also requires that the organization's existing security measures, in addition to meeting security needs, also need to specifically check and prevent vulnerability issues within the life cycle links involved in various business scenarios to prevent advanced threats from frequently exploiting known and existing defects to constitute risk events. In addition, the large number of devices and personnel involved in each link of the data life cycle also increases the possibility of data security risks. Taking the data storage link in the credit business scenario as an example, identity authentication, document upload, real-name authentication, liveness recognition and other actions all involve data storage. In actual business operations, data may be processed by different systems and stored in different locations. The security measures in any location or link do not meet the security requirements (for example, sensitive data is stored in plain text, the database lacks audit and abnormal operation alarm mechanisms, etc.), resulting in defects being frequently and successfully exploited by threats (for example, illegal access by operation and maintenance personnel, sensitive data in external documents, etc.), posing a very high possibility of data security risks. In summary, the identification method based on data security risk factors can not only identify and analyze the direct impact of threats, vulnerabilities and existing security measures on the confidentiality, integrity and availability of data, and then analyze the possibility and impact of risks, but also analyze the impact of threats, vulnerabilities and existing security measures faced by data and its various life cycle stages on the business and data processing activities involved, prevent the situation where the identification of data security risks is delayed or deviated from the actual situation due to the failure to effectively associate and analyze various risk factors, promote subsequent risk disposal, and realize a closed-loop risk management system. In addition, based on this identification method, we can also get a glimpse of the development direction of technical tools related to data security risk prevention and control in the future: based on the dynamic changes of data assets, threats, vulnerabilities, and existing security measures, data security risk detection tools should associate multi-dimensional information such as users, devices, and applications, identify and analyze the status of data security risk factors, and fully obtain data information at the data collection layer. At the processing layer and engine analysis layer, elements that affect key data security risk factors such as accounts, users, devices, applications, data, and IP should be extracted, and data standardization processing should be performed on the collected logs or the collected traffic should be parsed and information extracted. Further discovery and identification of sensitive data, analysis of vulnerabilities and threats, and continuous verification and feedback of detection results should be performed. Finally, at the operation layer, through the time series tracking and profiling of the behaviors of all users and entities, a visual situational security operation is formed to visualize the data activities of users and entities.

[0020] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A network data security risk identification and assessment method, characterized by: The network data security risk identification and assessment method comprises the following steps: S1. Data security risk factors: Starting from the perspectives of the data security risk's impact object, inducing source, and asset exposure, the common data security risk factors are extracted and the correlation between risk factors is established. Data security risk factors include data, business, data processing activities, data carriers, threats, vulnerabilities, and existing security measures. S2. Data security requirements: The security requirements of data include confidentiality requirements, integrity requirements, availability requirements, controllability requirements, and compliance requirements. In the process of identifying and evaluating the impact of data security risks, on the one hand, it is necessary to judge the value of the data itself, and on the other hand, it is necessary to analyze the security requirements of the data. Specifically, the analysis is conducted by collecting information on the vulnerabilities faced by data in collection, storage, transmission, and processing activities, as well as the existing security measures. S3. Possibility of data security risks: Threats can cause potential damage to data and are an objective risk factor. By assigning values ​​to the motivation, ability, and frequency of occurrence of threats and combining vulnerabilities with existing security measures, we can analyze the possibility of risk occurrence. In the process of identifying and evaluating the possibility of data security risks, we need to analyze the possibility of threats based on the exploitability of vulnerabilities and existing security measures. The exploitability of vulnerabilities should be summarized and recorded based on existing past experience. Since different stages of the data lifecycle have inherent vulnerabilities, it is necessary to comprehensively analyze the inherent security flaws of the data lifecycle based on specific lifecycle stages; S4. Data security risk identification method based on multiple factors: Establish a risk identification model, which includes data security risks, data security risk factors and evaluation dimensions. Data security risks include impact and possibility of occurrence. The impact corresponds to data value, data security requirements and existing security measures under the data security risk factor unit. The possibility of occurrence corresponds to threats and inherent vulnerabilities in the entire life cycle under the data security risk factor unit. Data value corresponds to authenticity, consistency, and integrity under the evaluation dimension unit, and data security requirements correspond to confidentiality requirements, integrity requirements, availability requirements, controllability requirements, and compliance requirements under the evaluation dimension unit; The threat corresponds to the source, motivation, and capability under the evaluation latitude unit, and the inherent vulnerability of the whole life cycle corresponds to the scenario, equipment, and personnel under the evaluation latitude unit; S5. Data security risk assessment method: The risk identification model is used to identify the unit content under the evaluation dimension corresponding to the risk factor to determine the data security risk level. The higher the unit content judgment pass degree, the lower the data security risk level; The failed unit content is a risk, and the risk is evaluated based on the impact consequences corresponding to the risk content.

2. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S1, the data elements are explained as follows: data has value, which directly affects the degree of impact of data security risks. The value is generated based on consumption and use. The more consumption and use, the greater the value; The business elements are as follows: Business relies on data and data processing activities to realize business value. The degree of impact that directly affects data security risks; The elements of data processing activities are explained as follows: data processors collect, store, use, process, transmit, provide, and disclose data in order to realize business value. Data processing activities should comply with the requirements of data security laws and regulations. The threats and vulnerabilities faced by each activity will directly determine the possibility of data security risks.

3. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S1, the data carrier element is described as follows: Data processors carry out data processing activities based on data carriers, which are vulnerable and face threats at all times. Their security status will directly affect the possibility of data security risks occurring; The threat factors are explained as follows: As an objective risk source, the source, capability and frequency of threat will affect the probability of data security risk occurring.

4. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S1, the vulnerability elements are described as follows: the vulnerability can be exploited by threats, the harm induced by the exploitation will affect the degree of impact of the data security risk, and the cost of the exploitation will affect the probability of the data security risk.

5. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S1, the existing security measures elements are described as follows: the existing security measures serve data security protection and will affect the degree of impact of data security risks induced by vulnerabilities.

6. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S2, the confidentiality requirements are as follows: confidentiality protection of data throughout the entire life cycle, protection of sensitive data, and protection of keys throughout the entire life cycle; The integrity requirements are as follows: ensure that the data source is verifiable, data transmission and storage are complete, data calculation is reliable, and data is auditable.

7. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S2, the availability requirements are described as follows: data carrier availability, security analysis capability, original data consistency, and backup mechanism completeness.

8. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S2, the controllability requirements are as follows: data flow value and ownership protection across organizations and borders; The compliance requirements are described as follows: compliance with national laws, regulations, standards, etc., possible social and public safety impacts, and potential cultural inclusion issues.

9. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S3, inherent security defects include, but are not limited to, whether the scope of data collection is effectively controlled during the data collection process, as well as issues such as the quality and legality of the collected content.

10. A network data security risk identification and assessment method according to claim 1, characterized in that: In step S4, the authenticity, consistency and integrity of the data, on the one hand, determine the value of the data itself, and on the other hand, affect the manifestation of data security risks in specific business scenarios.

Images