Before using data from the organization's customer database for a new marketing application, the first step should be to determine what data is required by the application and for what purpose. This will help to ensure that the data collection and processing are relevant, necessary, and proportionate to the intended use, and that the data minimization principle is followed. Data minimization means that only the minimum amount of personal data needed to achieve a specific purpose should be collected and processed, and that any excess or irrelevant data should be deleted or anonymized1. This will also help to comply with the data privacy laws and regulations that apply to the organization, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which require organizations to inform data subjects about the types and purposes of data processing, and to obtain their consent if needed23.
Reference:
ISACA, Data Privacy Audit/Assurance Program, Control Objective 2: Data Minimization, p. 61 ISACA, GDPR Data Protection Impact Assessments, p. 4-52 ISACA, CCPA vs. GDPR: Similarities and Differences, p. 1-23

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.

A privacy risk assessment is a process of identifying, analyzing and evaluating the privacy risks associated with the collection, use, disclosure or retention of personal data. A privacy risk assessment is the best way to distinguish between a privacy risk and compliance risk, as it would help to determine the likelihood and impact of privacy incidents or breaches that could affect the rights and interests of the data subjects, as well as the legal obligations and responsibilities of the organization. A privacy risk assessment would also help to identify and implement appropriate controls and measures to mitigate or reduce the privacy risks and ensure compliance with privacy principles, laws and regulations. The other options are not as effective as conducting a privacy risk assessment in distinguishing between a privacy risk and compliance risk. Performing a privacy risk audit is a process of verifying and validating the effectiveness and adequacy of the privacy controls and measures implemented by the organization, but it does not necessarily identify or evaluate the privacy risks or compliance risks. Validating a privacy risk attestation is a process of confirming and certifying the accuracy and completeness of the privacy information or statements provided by the organization, but it does not necessarily identify or evaluate the privacy risks or compliance risks. Conducting a privacy risk remediation exercise is a process of implementing corrective actions or improvements to address the identified or reported privacy risks or compliance risks, but it does not necessarily distinguish between them1, p. 66-67 Reference: 1: CDPSE Review Manual (Digital Version)

Online behavioral tracking is a tracking technology associated with unsolicited targeted advertisements that presents the greatest privacy risk. Online behavioral tracking is a technique that collects and analyzes personal data about users' online activities, preferences, interests, and behaviors across different websites or platforms. Online behavioral tracking is used to create user profiles and deliver personalized or targeted advertisements that match users' needs or wants. Online behavioral tracking poses a privacy risk because it can invade users' privacy by collecting sensitive or intimate personal data without their knowledge or consent, such as health conditions, political views, sexual orientation, etc. Online behavioral tracking can also expose users to unwanted or inappropriate advertisements that may influence their decisions or actions. Reference: : CDPSE Review Manual (Digital Version), page 139

A privacy audit is a systematic and independent examination of an organization's privacy policies, procedures, practices, and controls to assess their compliance with applicable laws, regulations, standards, and best practices. A privacy audit may result in various outputs, such as findings, recommendations, observations, or opinions. Among the options given, the output that is most likely to trigger remedial action is the identification of deficiencies in how personal data is shared with third parties. This is because such deficiencies may pose significant risks to the privacy and security of the data subjects, as well as to the reputation and legal liability of the organization. Remedial action may include implementing contractual safeguards, technical measures, or organizational changes to ensure that third parties respect and protect the personal data they receive from the organization.