CISA-KR 문제 226
다음 중 비상시설에 있어서 가장 중요한 고려사항은 무엇입니까?
정답: C
Explanation
A contingency facility is a backup site that can be used to resume business operations in the event of a disaster or disruption at the primary site. The most important consideration for a contingency facility is that it is located a sufficient distance away from the primary site, so that it is not affected by the same event that caused the disruption. For example, if the primary site is damaged by a fire, flood, earthquake, or terrorist attack, the contingency facility should be in a different geographic area that is unlikely to experience the same hazard. This way, the organization can continue to provide its services and products to its customers and stakeholders without interruption.
The other options are not as important as the location of the contingency facility. The badge access controls, the number of business assets, and the identifiability of the sites are secondary factors that may affect the security and efficiency of the contingency facility, but they are not essential for its functionality. Therefore, option C is the correct answer.
References:
The Importance of Contingency Planning
WHO guidance for contingency planning
A contingency facility is a backup site that can be used to resume business operations in the event of a disaster or disruption at the primary site. The most important consideration for a contingency facility is that it is located a sufficient distance away from the primary site, so that it is not affected by the same event that caused the disruption. For example, if the primary site is damaged by a fire, flood, earthquake, or terrorist attack, the contingency facility should be in a different geographic area that is unlikely to experience the same hazard. This way, the organization can continue to provide its services and products to its customers and stakeholders without interruption.
The other options are not as important as the location of the contingency facility. The badge access controls, the number of business assets, and the identifiability of the sites are secondary factors that may affect the security and efficiency of the contingency facility, but they are not essential for its functionality. Therefore, option C is the correct answer.
References:
The Importance of Contingency Planning
WHO guidance for contingency planning
CISA-KR 문제 227
IS 감사인은 제안된 새 컴퓨터 하드웨어 인수를 감사하라는 요청을 받았습니다. 감사인의 주요 관심사는 다음과 같습니다.
정답: C
Explanation
The IS auditor's primary concern when auditing the proposed acquisition of new computer hardware is that a clear business case has been established. A business case is a document that justifies the need, feasibility, and benefits of a proposed project or investment. A clear business case can help to ensure that the acquisition of new computer hardware is aligned with the organization's goals, objectives, and requirements, and that it provides value for money and return on investment. The other options are not as important as establishing a clear business case, as they do not address the rationale or justification for acquiring new computer hardware.
References: CISA Review Manual, 27th Edition, page 467
The IS auditor's primary concern when auditing the proposed acquisition of new computer hardware is that a clear business case has been established. A business case is a document that justifies the need, feasibility, and benefits of a proposed project or investment. A clear business case can help to ensure that the acquisition of new computer hardware is aligned with the organization's goals, objectives, and requirements, and that it provides value for money and return on investment. The other options are not as important as establishing a clear business case, as they do not address the rationale or justification for acquiring new computer hardware.
References: CISA Review Manual, 27th Edition, page 467
CISA-KR 문제 228
다음 중 애플리케이션 컨테이너 인프라에 내재된 위험 요소는 무엇입니까?
정답: D
Explanation
Application containers are a form of operating system virtualization that share the same kernel as the host operating system. This means that any vulnerability or compromise in the kernel can affect all the containers running on the same host, as well as the host itself. Additionally, containers may have privileged access to the kernel resources and functions, which can pose a risk of unauthorized or malicious actions by the container processes. Therefore, securing the kernel is a critical aspect of application container security.
Shared registries (option A) are not an inherent risk in the application container infrastructure, but they are a potential risk that depends on how they are configured and managed. Shared registries are repositories that store and distribute container images. They can be public or private, and they can have different levels of security and access controls. Shared registries can pose a risk of exposing sensitive data, distributing malicious or vulnerable images, or allowing unauthorized access to images. However, these risks can be mitigated by using secure connections, authentication and authorization mechanisms, image signing and scanning, and encryption.
Host operating system (option B) is not an inherent risk in the application container infrastructure, but it is a potential risk that depends on how it is configured and maintained. Host operating system is the underlying platform that runs the application containers and provides them with the necessary resources and services.
Host operating system can pose a risk of exposing vulnerabilities, misconfigurations, or malware that can affect the containers or the host itself. However, these risks can be mitigated by using minimal and hardened operating systems, applying patches and updates, enforcing security policies and controls, and isolating and monitoring the host.
Shared data (option C) is not an inherent risk in the application container infrastructure, but it is a potential risk that depends on how it is stored and accessed. Shared data is the information that is used or generated by the application containers and that may be shared among them or with external entities. Shared data can pose a risk of leaking confidential or sensitive data, corrupting or losing data integrity, or violating data privacy or compliance requirements. However, these risks can be mitigated by using secure storage solutions, encryption and decryption mechanisms, access control and auditing policies, and backup and recovery procedures.
Therefore, option D is the correct answer.
References:
Application Container Security Guide | NIST
CSA for a Secure Application Container Architecture
Application Container Security: Risks and Countermeasures
Application containers are a form of operating system virtualization that share the same kernel as the host operating system. This means that any vulnerability or compromise in the kernel can affect all the containers running on the same host, as well as the host itself. Additionally, containers may have privileged access to the kernel resources and functions, which can pose a risk of unauthorized or malicious actions by the container processes. Therefore, securing the kernel is a critical aspect of application container security.
Shared registries (option A) are not an inherent risk in the application container infrastructure, but they are a potential risk that depends on how they are configured and managed. Shared registries are repositories that store and distribute container images. They can be public or private, and they can have different levels of security and access controls. Shared registries can pose a risk of exposing sensitive data, distributing malicious or vulnerable images, or allowing unauthorized access to images. However, these risks can be mitigated by using secure connections, authentication and authorization mechanisms, image signing and scanning, and encryption.
Host operating system (option B) is not an inherent risk in the application container infrastructure, but it is a potential risk that depends on how it is configured and maintained. Host operating system is the underlying platform that runs the application containers and provides them with the necessary resources and services.
Host operating system can pose a risk of exposing vulnerabilities, misconfigurations, or malware that can affect the containers or the host itself. However, these risks can be mitigated by using minimal and hardened operating systems, applying patches and updates, enforcing security policies and controls, and isolating and monitoring the host.
Shared data (option C) is not an inherent risk in the application container infrastructure, but it is a potential risk that depends on how it is stored and accessed. Shared data is the information that is used or generated by the application containers and that may be shared among them or with external entities. Shared data can pose a risk of leaking confidential or sensitive data, corrupting or losing data integrity, or violating data privacy or compliance requirements. However, these risks can be mitigated by using secure storage solutions, encryption and decryption mechanisms, access control and auditing policies, and backup and recovery procedures.
Therefore, option D is the correct answer.
References:
Application Container Security Guide | NIST
CSA for a Secure Application Container Architecture
Application Container Security: Risks and Countermeasures
CISA-KR 문제 229
IS 감사자는 조직의 비즈니스 인텔리전스 인프라를 검토하고 있습니다. 조직이 합리적인 수준의 데이터 품질을 달성하는 데 도움이 되는 최선의 권장 사항은 다음과 같습니다.
정답: D
Explanation
This is because analyzing the data against predefined specifications is a method of data quality assessment that can help the organization achieve a reasonable level of data quality. Data quality assessment is the process of measuring and evaluating the accuracy, completeness, consistency, timeliness, validity, and usability of the data. Predefined specifications are the criteria or standards that define the expected or desired quality of the data. By comparing the actual data with the predefined specifications, the organization can identify and quantify any gaps, errors, or deviations in the data quality, and take corrective actions accordingly12.
Reviewing data against data classification standards (A) is not the best answer, because it is not a method of data quality assessment, but rather a method of data security management. Data classification standards are the rules or guidelines that define the level of sensitivity and confidentiality of the data, and determine the appropriate security and access controls for the data. For example, data can be classified into public, internal, confidential, or restricted categories. Reviewing data against data classification standards can help the organization protect the data from unauthorized or inappropriate use or disclosure, but it does not directly improve the data quality3.
Outsourcing data cleansing to skilled service providers (B) is not the best answer, because it is not a recommendation to help the organization achieve a reasonable level of data quality, but rather a decision to delegate or transfer the responsibility of data quality management to external parties. Data cleansing is the process of detecting and correcting any errors, inconsistencies, or anomalies in the data. Skilled service providers are third-party vendors or contractors that have the expertise and resources to perform data cleansing tasks. Outsourcing data cleansing to skilled service providers may have some benefits, such as cost savings, efficiency, or scalability, but it also has some risks, such as loss of control, dependency, or liability4.
Consolidating data stored across separate databases into a warehouse is not the best answer, because it is not a method of data quality assessment, but rather a method of data integration and storage. Data integration is the process of combining and transforming data from different sources and formats into a unified and consistent view. Data warehouse is a centralized repository that stores integrated and historical data for analytical purposes. Consolidating data stored across separate databases into a warehouse can help the organization improve the availability and accessibility of the data, but it does not necessarily improve the data quality.
This is because analyzing the data against predefined specifications is a method of data quality assessment that can help the organization achieve a reasonable level of data quality. Data quality assessment is the process of measuring and evaluating the accuracy, completeness, consistency, timeliness, validity, and usability of the data. Predefined specifications are the criteria or standards that define the expected or desired quality of the data. By comparing the actual data with the predefined specifications, the organization can identify and quantify any gaps, errors, or deviations in the data quality, and take corrective actions accordingly12.
Reviewing data against data classification standards (A) is not the best answer, because it is not a method of data quality assessment, but rather a method of data security management. Data classification standards are the rules or guidelines that define the level of sensitivity and confidentiality of the data, and determine the appropriate security and access controls for the data. For example, data can be classified into public, internal, confidential, or restricted categories. Reviewing data against data classification standards can help the organization protect the data from unauthorized or inappropriate use or disclosure, but it does not directly improve the data quality3.
Outsourcing data cleansing to skilled service providers (B) is not the best answer, because it is not a recommendation to help the organization achieve a reasonable level of data quality, but rather a decision to delegate or transfer the responsibility of data quality management to external parties. Data cleansing is the process of detecting and correcting any errors, inconsistencies, or anomalies in the data. Skilled service providers are third-party vendors or contractors that have the expertise and resources to perform data cleansing tasks. Outsourcing data cleansing to skilled service providers may have some benefits, such as cost savings, efficiency, or scalability, but it also has some risks, such as loss of control, dependency, or liability4.
Consolidating data stored across separate databases into a warehouse is not the best answer, because it is not a method of data quality assessment, but rather a method of data integration and storage. Data integration is the process of combining and transforming data from different sources and formats into a unified and consistent view. Data warehouse is a centralized repository that stores integrated and historical data for analytical purposes. Consolidating data stored across separate databases into a warehouse can help the organization improve the availability and accessibility of the data, but it does not necessarily improve the data quality.
CISA-KR 문제 230
조직은 타사 서비스 공급자로부터 엔터프라이즈 데이터 웨어하우스로 자동화된 데이터 피드를 수신할 계획입니다. 다음 중 불량 데이터 수락을 방지하는 가장 좋은 방법은 무엇입니까?
정답: D
Explanation
The best way to prevent accepting bad data from a third-party service provider is to implement business rules to reject invalid data. Business rules are logical statements that define the data quality requirements and standards for the organization. By implementing business rules, the organization can ensure that only data that meets the predefined criteria is accepted into the enterprise data warehouse. Obtaining error codes indicating failed data feeds, purchasing data cleansing tools from a reputable vendor, and appointing data quality champions across the organization are useful measures to improve data quality, but they do not prevent accepting bad data in the first place. References: ISACA Journal Article: Data Quality Management
The best way to prevent accepting bad data from a third-party service provider is to implement business rules to reject invalid data. Business rules are logical statements that define the data quality requirements and standards for the organization. By implementing business rules, the organization can ensure that only data that meets the predefined criteria is accepted into the enterprise data warehouse. Obtaining error codes indicating failed data feeds, purchasing data cleansing tools from a reputable vendor, and appointing data quality champions across the organization are useful measures to improve data quality, but they do not prevent accepting bad data in the first place. References: ISACA Journal Article: Data Quality Management
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