Isaca CDPSE - Certified Data Privacy Solutions Engineer

The practice that best indicates an organization follows the data minimization principle is that data is regularly reviewed for its relevance. The data minimization principle is one of the core principles of data protection under various laws and regulations, such as the GDPR or the CCPA. It states that personal data should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. By regularly reviewing the data they hold, organizations can ensure that they do not collect or retain excessive or unnecessary data that may pose privacy risks or violate data subject rights.

Data is pseudonymized when being backed up, data is encrypted before storage, or data is only accessible on a need-to-know basis are also good practices for data protection, but they do not directly indicate that the organization follows the data minimization principle. Pseudonymization is a process of replacing identifying information in data with artificial identifiers or pseudonyms. Pseudonymization can help enhance the privacy of data by reducing the linkability between data and data subjects, but it does not prevent re-identification or inference attacks. Encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Encryption can help protect the confidentiality, integrity, and availability of data by preventing unauthorized access, disclosure, or modification. Access control is a process of restricting who can access, modify, or delete data based on their roles, permissions, or credentials. Access control can help prevent unauthorized or inappropriate use of data by limiting the scope of access.

CDPSE/ISACA risk response taxonomy defines risk avoidance as deciding not to engage in the activity that gives rise to the risk. Reduction/mitigation (B/D) means proceed with controls; acceptance (C) means proceed without additional treatment. Not expanding is classic avoidance.

Key CDPSE-aligned phrasing (short extract): “Risk avoidance: Discontinue or do not initiate activities that create risk.”

Continuous monitoring dashboards provide ongoing, real-time visibility into privacy posture, allowing senior management to track performance trends and risk levels over time. PIAs (B) are project-specific and point-in-time; metadata inventories (A) document assets but are not monitoring tools; vulnerability results (C) are technical and periodic, not enterprise-level posture tracking.

“Dashboards provide ongoing, measurable insights into privacy compliance and posture for executive oversight.”

Data privacy and data security are related but distinct concepts that are both essential for protecting personal data. Data privacy is about ensuring that personal data are collected, used, shared and disposed of in a lawful, fair and transparent manner, respecting the rights and preferences of the data subjects. Data privacy also involves implementing policies, procedures and controls to comply with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). Data privacy protects users from unauthorized disclosure of their personal data, which may result in harm, such as identity theft, fraud, discrimination or reputational damage.

Data security is about safeguarding the confidentiality, integrity and availability of data from unauthorized or malicious access, use, modification or destruction. Data security also involves implementing technical and organizational measures to prevent or mitigate data breaches or incidents, such as encryption, authentication, backup or incident response. Data security prevents compromise of data, which may result in loss, corruption or disruption of data.

Obtaining user consent is the best way to mitigate the privacy risk associated with setting cookies on a website. This means that the website should inform the users about the purpose, type, and duration of the cookies, and ask for their permission before storing or accessing any cookies on their browsers. This way, the users can exercise their right to control their personal data and opt-in or opt-out of cookies as they wish.

According to the General Data Protection Regulation (GDPR), consent must be freely given, specific, informed, and unambiguous. The website should provide clear and easy-to-understand information about the cookies and their implications for the users’ privacy, and offer a simple and effective way for the users to indicate their consent or refusal. The website should also respect the users’ choice and allow them to withdraw their consent at any time.

Implementing impersonation, ensuring nonrepudiation, and applying data masking are not relevant or effective methods to mitigate the privacy risk associated with setting cookies on a website. Impersonation means accessing or using data on behalf of another user, which could violate their privacy and security. Nonrepudiation means providing proof of the origin, authenticity, and integrity of data, which does not address the issue of user consent or preference. Data masking means hiding or replacing sensitive data with fake or modified data, which does not prevent the storage or access of cookies on the user’s browser.

CDPSE emphasizes outcomes of training—measurable role-based understanding and behavior—over mere completion or absence of incidents. HR mandates (B) and clean audits (D) show activity or point-in-time results, not sustained effectiveness. No incidents (A) is not a reliable indicator of program quality.

Key CDPSE-aligned phrasing (short extract): “Effective awareness is role-based and demonstrated in behavior.”

User behavior analytics tools are the best control to detect potential internal breaches of personal data because they monitor and analyze the activities and patterns of users on the network and systems, and alert or block any anomalous or suspicious behavior that may indicate unauthorized access, misuse or exfiltration of personal data. Data loss prevention (DLP) systems, employee background checks and classification of data are useful controls to prevent or mitigate internal breaches of personal data, but they do not necessarily detect them.

A raw zone is a zone within a data lake that contains unprocessed or unstructured data that is ingested from various sources without any transformation or validation. A raw zone may contain sensitive data that has not been identified or classified yet, such as personal data. Therefore, sensitive data in a raw zone should be encrypted or tokenized to protect its confidentiality and integrity. Encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Tokenization is a process of replacing sensitive data with non-sensitive substitutes called tokens. Both encryption and tokenization help to prevent unauthorized or unlawful access, use, disclosure, or transfer of sensitive data in a raw zone. References: : CDPSE Review Manual (Digital Version), page 169

Critical data elements are the data elements that are essential for the organization to achieve its business objectives, comply with legal and regulatory requirements, and protect the privacy and security of the data subjects. Critical data elements should be mapped to the data process flow, which is a graphical representation of how data is collected, processed, stored, shared, and disposed of within the organization. Mapping critical data elements to the data process flow helps to identify the sources, destinations, transformations, and dependencies of the data, as well as the potential risks and controls associated with each step of the data lifecycle.

The most important consideration for developing data retention requirements is the applicable regulations that govern the data. Different types of data may be subject to different legal and regulatory obligations, such as how long the data must be kept, how it must be protected, and how it can be accessed or disposed of. Failing to comply with these obligations can result in fines, penalties, lawsuits, or reputational damage for the organization. Therefore, it is essential to identify and follow the applicable regulations for each data category.