Isaca Updated CDPSE Exam Questions and Answers by abdurrahman

The best way to maintain the quality of personal data is to implement routine automatic validation, which is a process of checking the accuracy, completeness, consistency, and timeliness of the data using automated tools or scripts. Routine automatic validation can help identify and correct any errors, anomalies, or discrepancies in the data, as well as ensure that the data meets the specified quality standards and requirements. Routine automatic validation can also help improve the efficiency and reliability of the data processing and analysis12.

References:

• CDPSE Exam Content Outline, Domain 3 – Data Lifecycle (Data Quality), Task 2: Implement data quality measures3.
• CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.2 – Data Quality4.

The best way to ensure an effective data privacy policy is implemented is to align regulatory requirements with business needs, because this will help achieve compliance while also supporting the organization’s objectives, values, and strategies. A data privacy policy should reflect the legal obligations and expectations of the organization, as well as the needs and preferences of its stakeholders, such as customers, employees, partners, and regulators. A data privacy policy should also be flexible and adaptable to changing circumstances and environments12.

References:

• CDPSE Exam Content Outline, Domain 1 – Privacy Governance (Governance, Management & Risk Management), Task 3: Participate in the evaluation of privacy policies, programs and policies for their alignment with legal requirements, regulatory requirements and/or industry best practices3.
• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.2 – Privacy Policy4.

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.

Prioritizing privacy-related risk scenarios as part of ERM processes is the best way to ensure that the risk responses meet the organizational objectives, because it helps to align the privacy risk management with the overall strategic goals, values, and culture of the organization. ERM is a holistic approach to identify, assess, and manage risks across the organization, taking into account the interdependencies and trade-offs among different types of risks. By integrating privacy-related risk scenarios into the ERM processes, the organization can evaluate the potential impact and likelihood of privacy risks on its mission, vision, and performance, and prioritize the most significant ones for mitigation or acceptance. This can also help to allocate appropriate resources, assign clear roles and responsibilities, and monitor and report on the effectiveness of the risk responses.

References:

• Privacy Risk Management, ISACA Journal
• Enterprise Risk Assessment, Deloitte