CDPSE Exam Dumps - Isaca Certification Questions and Answers

Data sanitization is a process of permanently erasing or destroying data from a storage device or media to prevent unauthorized access or recovery of the data. Data sanitization methods can include physical destruction, degaussing, overwriting, encryption or cryptographic erasure. The most important consideration when using advanced data sanitization methods to ensure privacy data will be unrecoverable is the type of media on which the data is stored, as different media types may require different methods or techniques to achieve effective sanitization. For example, physical destruction may be suitable for optical disks or tapes, but not for solid state drives (SSDs) or flash memory devices. Degaussing may be effective for magnetic disks or tapes, but not for optical disks or SSDs. Overwriting may work for hard disk drives (HDDs) or SSDs, but not for tapes or optical disks. Encryption or cryptographic erasure may be applicable for any media type, but may require additional security measures to protect the encryption keys or certificates. The other options are not as important as the type of media when using advanced data sanitization methods. Subject matter expertise may be helpful, but not essential, as long as the appropriate method is selected and applied correctly. Regulatory compliance requirements may influence the choice of method, but not necessarily determine it, as different methods may meet different standards or criteria. Location of data may affect the feasibility or cost of applying a method, but not its effectiveness or suitability., p. 93-94 References: : CDPSE Review Manual (Digital Version)

Data classification is the process of categorizing data according to its sensitivity, value, and criticality for the organization and the data subjects. Data classification has the greatest impact on the treatment of data within the scope of an organization’s privacy policy, as it determines the appropriate level of protection, access, retention, and disposal for each type of data. Data classification also helps to comply with the privacy principles and regulations, such as data minimization, purpose limitation, accuracy, security, and accountability.

References: CDPSE Review Manual, 2021, p. 80

A multinational organization that operates across different countries and regions should perform an annual review of changes to privacy regulations in all jurisdictions where its corporate data is processed. This is because different jurisdictions may have different privacy laws and requirements that apply to the collection, use, storage, transfer, and disposal of personal data. For example, the EU General Data Protection Regulation (GDPR) applies to any organization that processes personal data of individuals in the EU, regardless of where the organization is located or where the data is processed. Therefore, the organization should keep track of the changes to privacy regulations in all relevant jurisdictions and update its data privacy policy accordingly to ensure compliance and avoid penalties or lawsuits.

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.

References: CDPSE Review Manual, 2021, p. 83

The most important consideration when writing an organization’s privacy policy is to align the statements to the organizational practices, because this will help ensure that the policy is accurate, consistent, and transparent. A privacy policy is a document that explains how the organization collects, uses, discloses, and protects personal data from its customers, employees, partners, and other stakeholders. A privacy policy should reflect the actual data processing activities and privacy measures of the organization, as well as comply with the applicable laws and regulations. A privacy policy that is not aligned with the organizational practices may lead to confusion, mistrust, or legal liability12.

References:

• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.2 – Privacy Policy3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 1 – Privacy Governance, Section 1.2 – Data Privacy Laws and Regulations4.

The most important thing to consider when managing changes to the provision of services by a third party that processes personal data is the business impact due to the changes. Changes to the provision of services by a third party can affect the organization’s ability to meet its business objectives and legal obligations related to data processing activities. For example, changes to the service level agreement (SLA), the scope of services, the security measures, the location of servers, etc., can have implications for the quality, availability, confidentiality, integrity, and compliance of personal data processing. Therefore, an IT privacy practitioner should assess and evaluate the business impact due to the changes, and ensure that they are aligned with the organization’s privacy policies and applicable privacy regulations and standards. References: : CDPSE Review Manual (Digital Version), page 41

Inferred data is the type of data that is produced by using a more complex method of analytics to find correlations between data sets and using them to categorize or profile people. Inferred data is not directly observed or collected from the data subjects, but rather derived from other sources of data, such as behavioral, transactional, or demographic data. Inferred data can be used to make assumptions or predictions about the data subjects’ preferences, interests, behaviors, or characteristics12.

References:

• CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.1 – Data Classification3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3 – Data Lifecycle, Section 3.2 – Data Classification4.

User behavior analytics is a technology that uses data analysis and machine learning to monitor, detect and respond to anomalous or malicious user activities, such as accessing sensitive personal customer information to use for unauthorized purposes. User behavior analytics is the best choice to mitigate this risk, as it would help to identify and prevent insider threats, data breaches, fraud or misuse of data by authorized individuals. User behavior analytics can also help to enforce policies and controls, such as access control, audit trail or data loss prevention. The other options are not as effective as user behavior analytics in mitigating this risk. Email filtering system is a technology that scans and blocks incoming or outgoing emails that contain spam, malware or phishing attempts, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes. Intrusion monitoring is a technology that monitors and alerts on unauthorized or malicious attempts to access a system or network, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes. Mobile device management (MDM) is a technology that manages and secures mobile devices that are used to access or store organizational data, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes1, p. 92 References: 1: CDPSE Review Manual (Digital Version)

National data privacy legislative and regulatory requirements in each relevant jurisdiction are the most important data protection consideration for a global organization that is planning to implement a customer relationship management (CRM) system to be used in offices based in multiple countries, as they would determine the legal obligations and responsibilities of the organization with respect to the collection, use, disclosure and transfer of customer personal data across different jurisdictions. National data privacy legislative and regulatory requirements may vary significantly from country to country, depending on the type or nature of personal data or data processing activities, and may impose different rules and standards for obtaining consent, providing notice, ensuring security, enforcing rights, reporting breaches, appointing representatives or transferring data. The organization would need to comply with the national data privacy legislative and regulatory requirements in each relevant jurisdiction where it operates or where its customers are located, and to implement appropriate measures and safeguards to ensure compliance. The other options are not as important as national data privacy legislative and regulatory requirements in each relevant jurisdiction as data protection considerations for a global organization that is planning to implement a CRM system to be used in offices based in multiple countries. Industry best practice related to information security standards in each relevant jurisdiction may provide some guidance or benchmarks for ensuring security of customer personal data, but they may not reflect the specific context or needs of the organization or the customers, or comply with the legal obligations and responsibilities of the organization. Identity and access management mechanisms to restrict access based on need to know may help to protect customer personal data from unauthorized access, modification or disclosure by internal or external parties, but they may not address other aspects of data protection, such as consent, notice, rights, breaches, representatives or transfers. Encryption algorithms for securing customer personal data at rest and in transit may help to protect customer personal data from unauthorized access, modification or disclosure by internal or external parties, but they may not address other aspects of data protection, such as consent, notice, rights, breaches, representatives or transfers1, p. 63-64 References: 1: CDPSE Review Manual (Digital Version)