CDPSE Exam Dumps - Isaca Certification Questions and Answers

Conducting a privacy impact assessment (PIA) should be done first to establish privacy by design when developing a contact-tracing application. A PIA is a systematic process that identifies and evaluates the potential effects of personal data processing operations on the privacy of individuals and the organization. A PIA helps to identify privacy risks and mitigation strategies at an early stage of development and ensures compliance with legal and regulatory requirements. Conducting a development environment review, identifying privacy controls, or identifying differential privacy techniques are important steps in privacy by design, but they should be done after conducting a PIA. References: CDPSE Exam Content Outline, Domain 2, Task 2.1

A data dictionary is a document that defines and describes the data elements, attributes, formats, sources, destinations, purposes and relationships of a data set or system. A data dictionary would be the best way to ensure personal data usage is standardized across the entire organization, as it would provide a common and consistent understanding and reference for how personal data is collected, used, disclosed and transferred within and outside the organization. A data dictionary would also help to ensure compliance with privacy principles, such as accuracy, transparency and accountability. The other options are not as effective as developing a data dictionary in ensuring personal data usage is standardized across the entire organization. De-identify all data is a technique that removes or modifies direct and indirect identifiers in a data set to prevent or limit the identification of the data subjects, but it does not ensure standardization or consistency of personal data usage across the organization. Encrypt all sensitive data is a technique that transforms plain text data into cipher text using an algorithm and a key, making it unreadable by unauthorized parties, but it does not ensure standardization or consistency of personal data usage across the organization. Perform data discovery is a process of identifying and locating personal data within an organization’s systems, databases, applications or files, but it does not ensure standardization or consistency of personal data usage across the organization1, p. 69-70 References: 1: CDPSE Review Manual (Digital Version)

The best way to ensure data confidentiality across databases is to use data anonymization, which is a process of removing or modifying personal or sensitive data from a dataset so that it cannot be linked or attributed to a specific individual or entity. Data anonymization helps protect the privacy and security of the data subjects, as well as comply with the applicable data protection laws and regulations. Data anonymization can be achieved by using various techniques, such as masking, encryption, aggregation, generalization, perturbation, or synthetic data generation12.

A privacy impact assessment (PIA) is a systematic process to identify and evaluate the potential privacy impacts of a system, project, program or initiative that involves the collection, use, disclosure or retention of personal data. A PIA should be done first to address privacy risk when migrating customer relationship management (CRM) data to a new system, as it would help to ensure that privacy risks are identified and mitigated before the migration is executed. A PIA would also help to ensure compliance with privacy principles, laws and regulations, and alignment with customer expectations and preferences. The other options are not as important as performing a PIA when addressing privacy risk when migrating CRM data to a new system. Developing a data migration plan is a process of defining and documenting the objectives, scope, approach, methods and steps for transferring data from one system to another, but it does not necessarily address privacy risk or impact. Conducting a legitimate interest analysis (LIA) is a process of assessing whether there is a legitimate interest for processing personal data that outweighs the rights and interests of the data subjects, but it is only applicable in certain jurisdictions and situations where legitimate interest is a valid legal basis for processing. Obtaining consent from data subjects is a process of obtaining their permission or agreement before collecting, using, disclosing or transferring their personal data for specific purposes, but it may not be required or sufficient for migrating CRM data to a new system, depending on the context and nature of the migration and the applicable laws and regulations1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

The first step for an IT privacy practitioner following a decision to expand remote working capability is to evaluate the impact resulting from this change on the organization’s privacy policies, programs and practices. This will help identify the risks and gaps that need to be addressed, as well as the opportunities for improvement and optimization. The other options are possible actions that may be taken after the impact assessment, depending on the results and recommendations.

The best way to address the concern that data collected by a third-party vendor and provided back to the organization may not be protected according to the organization’s privacy notice is to validate contract compliance. This means that the organization should verify that the third-party vendor is adhering to the terms and conditions of the contract, which should include clauses on data protection, privacy, and security. The contract should also specify the obligations and responsibilities of both parties regarding data collection, processing, storage, transfer, retention, and disposal. By validating contract compliance, the organization can ensure that the third-party vendor is following the same privacy standards and practices as the organization.

Data masking is the process of hiding original data with modified content to protect sensitive data from unauthorized access or disclosure. Data masking is often used for testing purposes in non-production environments, where personal data is not needed or allowed. However, data masking can pose several challenges, especially for a global financial institution that has multiple interconnected systems and applications. One of the greatest challenges is to preserve the complex relationships within and across systems while masking the data. This means that the masked data must maintain the same format, referential integrity, semantic integrity, and uniqueness as the original data, so that the testing results are valid and reliable. For example, if a customer’s name is masked in one system, it must be masked consistently in all other systems that reference it. If a transaction amount is masked in one system, it must not violate any business rules or constraints in another system. If a credit card number is masked in one system, it must still be a valid credit card number in another system. Preserving these complex relationships can be challenging because it requires a thorough understanding of the data model, the business logic, and the dependencies among systems. It also requires a robust and flexible data masking tool that can handle different types of data and platforms.