Which of the following is the BEST way to explain the difference between data privacy and data security?
Correct Answer: D
Explanation 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. References: * The Difference Between Data Privacy and Data Security - ISACA, section 1: "Data privacy is focused on the use and governance of personal data-things like putting policies in place to ensure that consumers' personal information is being collected, shared and used in appropriate ways." * Practical Data Security and Privacy for GDPR and CCPA - ISACA, section 1: "Data security is the practice of protecting digital information from unauthorized access, corruption or theft throughout its life cycle."
Question 27
An organization has a policy requiring the encryption of personal data if transmitted through email. Which of the following is the BEST control to ensure the effectiveness of this policy?
Correct Answer: B
Explanation A data loss prevention (DLP) tool is a software solution that monitors, detects and prevents the unauthorized transmission or leakage of sensitive data, such as personal data, from an organization's network or devices. A DLP tool can help to ensure the effectiveness of a policy requiring the encryption of personal data if transmitted through email, by applying the following controls: * Scanning the content and attachments of outgoing emails for personal data, such as names, email addresses, biometric data, IP addresses, etc. * Blocking or quarantining emails that contain unencrypted personal data, and alerting the sender and/or the administrator of the policy violation. * Encrypting personal data automatically before sending them through email, using encryption standards and algorithms that are compliant with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). * Generating audit logs and reports of email activities and incidents involving personal data, and providing visibility and accountability for policy compliance. The other options are less effective or irrelevant to ensure the effectiveness of the policy. Providing periodic user awareness training on data encryption is a good practice, but it does not guarantee that users will follow the policy or know how to encrypt personal data properly. Conducting regular control self-assessments (CSAs) is a useful method to evaluate the design and operation of the policy, but it does not prevent or detect policy violations in real time. Enforcing annual attestation to policy compliance is a formal way to demonstrate user commitment to the policy, but it does not verify or measure the actual level of compliance. References: * The Complexity Conundrum: Simplifying Data Security - ISACA, section 3: "Data loss prevention (DLP) solutions can help prevent unauthorized access to sensitive information by monitoring network traffic for specific keywords or patterns." * Guide to Securing Personal Data in Electronic Medium, section 3.2: "Organisations should consider implementing DLP solutions to prevent unauthorised disclosure of personal data via email." * Encryption in the Hands of End Users - ISACA, section 2: "A key goal of encryption is to protect the file even when direct access is possible or the transfer is intercepted."
Question 28
Which type of data is produced by using a more complex method of analytics to find correlations between data sets and using them to categorize or profile people?
Correct Answer: B
Explanation 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.
Question 29
Which of the following processes BEST enables an organization to maintain the quality of personal data?
Correct Answer: B
Question 30
A migration of personal data involving a data source with outdated documentation has been approved by senior management. Which of the following should be done NEXT?
Correct Answer: B
Explanation Ensuring appropriate data classification should be done next after a migration of personal data involving a data source with outdated documentation has been approved by senior management, as it helps to identify the types, locations, and owners of the data, and to apply the appropriate privacy controls and measures based on the data classification level. Data classification also facilitates the data discovery, data minimization, data retention, and data disposal processes15. References: 1 Domain 3, Task 2; 5 Page 9
