Isaca Updated CISA Exam Questions and Answers by moshe

 Data classification is the process of organizing and labeling data into categories based on file type, contents, and other metadata. Data classification helps organizations answer important questions about their data that inform how they mitigate risk and manage data governance policies. Data classification also enables appropriate protection measures, and efficient search, retrieval and use of each data category12.

While evaluating the data classification process of an organization, an IS auditor’s primary focus should be on whether data is correctly classified. This means that the data is assigned to the appropriate classification level based on its sensitivity, importance, integrity, availability, compliance requirements, and business value. Correct data classification ensures that the data is protected according to its risk level, and that the organization can comply with relevant laws and regulations that apply to different types of data3.

The other three options are not the primary focus of an IS auditor while evaluating the data classification process, although they may be relevant or useful for certain aspects of data management. Data classifications are automated means that the organization uses software tools or algorithms to analyze and label data based on predefined rules or criteria. This can improve the efficiency and consistency of data classification, but it does not guarantee that the data is correctly classified. The IS auditor still needs to verify the accuracy and validity of the automated classifications, and check for any errors or anomalies.

A data dictionary is maintained means that the organization keeps a record of the definitions, formats, sources, and relationships of the data elements in its systems or databases. This can enhance the understanding and usability of the data, but it does not ensure that the data is correctly classified. The IS auditor still needs to examine the content and context of the data, and compare it with the classification criteria and policies.

Data retention requirements are clearly defined means that the organization specifies how long it will keep different types of data, and when it will delete or archive them. This can help reduce storage costs, improve performance, and comply with legal obligations, but it does not ensure that the data is correctly classified. The IS auditor still needs to assess whether the data is stored and protected according to its classification level, and whether the retention periods are appropriate for each type of data.

Therefore, data is correctly classified is the best answer.

References:

• Data Classification: The Basics and a 6-Step Checklist - NetApp
• What is Data Classification? Guidelines and Process - Varonis
• Data Classification and Handling Procedures Guide

Validation checks are a type of data quality control that helps to ensure the integrity of data for a system interface. Validation checks verify that the data entered or transferred between systems is correct, consistent, and conforms to predefined rules or standards. Validation checks can prevent or detect errors, anomalies, or inconsistencies in the data that may affect the system’s functionality, performance, or security.

Option C is correct because validation checks are a common and effective method of ensuring data integrity for a system interface. Validation checks can be performed at various stages of the data lifecycle, such as input, processing, output, or storage. Validation checks can also be applied to different types of data, such as data types, codes, ranges, formats, consistency, and uniqueness.

Option A is incorrect because system interface testing is a type of software testing that verifies the interaction between two separate systems or components of a system. System interface testing does not directly ensure the integrity of data for a system interface, but rather the functionality and reliability of the interface itself. System interface testing may use validation checks as part of its test cases, but it is not the same as validation checks.

Option B is incorrect because user acceptance testing (UAT) is a type of software testing that evaluates whether the system meets the user’s expectations and requirements. UAT does not directly ensure the integrity of data for a system interface, but rather the usability and acceptability of the system from the user’s perspective. UAT may use validation checks as part of its test scenarios, but it is not the same as validation checks.

Option D is incorrect because audit logs are records of events and activities that occur within a system or network. Audit logs do not directly ensure the integrity of data for a system interface, but rather provide evidence and accountability for the system’s operations and security. Audit logs may use validation checks as part of their analysis or reporting, but they are not the same as validation checks.

References:

• CISA Online Review Course1, Module 5: Protection of Information Assets, Lesson 4: Data Quality Management, slide 5-6.
• CISA Review Manual (Digital Version)2, Chapter 5: Protection of Information Assets, Section 5.3: Data Quality Management, p. 281-282.
• CISA Review Manual (Print Version), Chapter 5: Protection of Information Assets, Section 5.3: Data Quality Management, p. 281-282.
• CISA Questions, Answers & Explanations Database3, Question ID: QAE_CISA_722.
• Data Validation - Overview, Types, Practical Examples4
• Data Validity: The Best Practice for Your Business5
• Validation - Data validation6
• What is Data Validation? Types, Techniques, Tools7

The first step when developing a DLP solution for a large organization is to conduct a data inventory and classification exercise. This step involves identifying and locating all the data assets that the organization owns, generates, or handles, and assigning them to different categories based on their sensitivity, value, and regulatory requirements1. Data inventory and classification is essential for DLP because it helps to determine the scope and objectives of the DLP solution, as well as the appropriate level of protection and monitoring for each data category2. Data inventory and classification also enables the organization to prioritize its DLP efforts based on the risk and impact of data loss or leakage3.

Option B is not correct because identifying approved data workflows across the enterprise is a subsequent step after conducting data inventory and classification. Data workflows are the processes and channels through which data are created, stored, accessed, shared, or transmitted within or outside the organization4. Identifying approved data workflows helps to define the normal and legitimate use of data, as well as to detect and prevent unauthorized or anomalous data activities5. However, before identifying approved data workflows, the organization needs to know what data it has and how it should be classified.

Option C is not correct because conducting a threat analysis against sensitive data usage is another subsequent step after conducting data inventory and classification. Threat analysis is the process of identifying and assessing the potential sources, methods, and impacts of data loss or leakage incidents. Threat analysis helps to design and implement effective DLP controls and countermeasures based on the risk profile of each data category. However, before conducting threat analysis, the organization needs to know what data it has and how it should be classified.

Option D is not correct because creating the DLP policies and templates is the final step after conducting data inventory and classification, identifying approved data workflows, and conducting threat analysis. DLP policies and templates are the rules and configurations that specify how the DLP solution should monitor, detect, report, and respond to data loss or leakage events. DLP policies and templates should be aligned with the organization’s business needs, regulatory obligations, and risk appetite. However, before creating the DLP policies and templates, the organization needs to know what data it has, how it should be classified, how it should be used, and what threats it faces.

References:

• Data Inventory & Classification: The First Step in Data Protection1
• Data Classification: What It Is And Why You Need It2
• How to Prioritize Your Data Loss Prevention Strategy in 20203
• What Is Data Workflow? Definition & Examples4
• How to Identify Data Workflows for Your Business5
• Threat Analysis: A Comprehensive Guide for Beginners
• How to Conduct a Threat Assessment for Your Business
• What Is Data Loss Prevention (DLP)? Definition & Examples
• How to Create Effective Data Loss Prevention Policies

A backlog consumption report is a report that shows the amount of work that has been completed and the amount of work that remains to be done in a project. It is a useful tool for measuring the progress and performance of a web-based customer service application development project, as it can indicate whether the project is on track, ahead or behind schedule, and how much effort is required to finish the project. A backlog consumption report can also help identify any issues or risks that may affect the project delivery. Critical path analysis reports, developer status reports and change management logs are also helpful for evaluating a project, but they are not as helpful as a backlog consumption report, as they do not provide a clear picture of the overall project status and completion rate. References:

• : [Backlog Consumption Report Definition]
• : Backlog Consumption Report | ISACA