Isaca Updated CDPSE Exam Questions and Answers by reagan

Pseudonymization is a technique that replaces direct identifiers in a data set with pseudonyms or artificial identifiers that do not reveal the identity of the data subjects. Pseudonymization reduces the linkability of the data set with the original identity of the data subjects and thus enhances the privacy and security of the data. However, pseudonymization is not irreversible and the original identity can be re-established if the pseudonym or key is compromised. Therefore, it is important to keep the identifier separate and distinct from the data it protects and to apply additional security measures to safeguard the identifier. The other options are not relevant to pseudonymization1, p. 74-75 References: 1: CDPSE Review Manual (Digital Version)

A data use policy is a document that defines the rules and guidelines for how personal data are collected, used, stored, shared and deleted by an organization. It is an important part of data governance and compliance, as it helps to ensure that personal data are handled in a lawful, fair and transparent manner, respecting the rights and preferences of data subjects. A data use policy should include the requirements for collecting and using personal data, such as the legal basis, the purpose, the scope, the consent, the data minimization, the accuracy, the security and the accountability. These requirements help to establish the legitimacy and necessity of data processing activities, and to prevent unauthorized or excessive use of personal data.

Obtaining executive support is the first step in developing an organization-wide strategy to address data privacy risk, as it ensures that the privacy program has the necessary resources, authority, and alignment with the organization’s goals and objectives. Without executive support, the privacy program may face challenges in implementing and enforcing privacy policies, procedures, and controls across the organization. References: 2 Domain 1, Task 1

Before an organization can respond to data subject access requests (DSARs), it needs to have a clear understanding of the data in its possession, such as what types of personal data are collected, where they are stored, how they are processed, who has access to them, and how long they are retained. This will help the organization to locate and retrieve the relevant data for each DSAR, and to ensure that the data are accurate, complete and up to date. Understanding the data in its possession will also help the organization to comply with other data protection principles and obligations, such as data minimization, purpose limitation, security and accountability.

The other options are less important or irrelevant to do first. Investing in a platform to automate data review may help to speed up the response process, but it does not guarantee that the organization has identified all the data sources and categories that are subject to DSARs. Confirming what is required for disclosure is also important, but it depends on the specific request and the applicable law or regulation. Creating a policy for handling access requests is a good practice, but it should be based on a thorough understanding of the data in its possession.