Free Isaca CDPSE Practice Exam with Questions & Answers | Set: 4

The best indication of a highly effective privacy training program is that members of the workforce understand their roles in protecting data privacy, because this shows that the training program has successfully raised the awareness and knowledge of the workforce on the importance, principles and practices of data privacy, and how they can contribute to the organization’s privacy objectives and compliance. According to ISACA, one of the key elements of a privacy training program is to define and communicate the roles and responsibilities of the workforce in relation to data privacy1. Members of the workforce who understand their roles in protecting data privacy are more likely to follow the privacy policies and procedures, report any privacy incidents or issues, and support the privacy culture of the organization2. Recent audits have no findings or recommendations related to data privacy, no privacy incidents have been reported in the last year, and HR has made privacy training an annual mandate for the organization are not as reliable as members of the workforce understand their roles in protecting data privacy, as they do not necessarily reflect the effectiveness of the privacy training program, but rather the performance of other factors such as audit processes, incident management systems, or HR policies.

Using hash values with stored personal data best enables an organization to detect changes to the data, because hash values are unique and fixed outputs that are generated from the data using a mathematical algorithm. If the data is altered in any way, even by a single bit, the hash value will change dramatically. Therefore, by comparing the current hash value of the data with the original or expected hash value, the organization can verify the integrity and authenticity of the data. If the hash values match, it means that the data has not been tampered with. If the hash values differ, it means that the data has been corrupted or modified.

References:

• Ensuring Data Integrity with Hash Codes, Microsoft Learn
• What is ‘hashing,’ and does it help avoid the obligations imposed by the new privacy regulations?, Data Privacy Dish

The best way to ensure that application hardening is included throughout the software development life cycle (SDLC) is to include qualified application security personnel as part of the process. Application hardening is the process of applying security measures and techniques to an application to reduce its attack surface, vulnerabilities, and risks. Application hardening should be integrated into every stage of the SDLC, from planning and design to development and testing to deployment and maintenance. Including qualified application security personnel as part of the process helps to ensure that application hardening is performed effectively and consistently, as well as to provide guidance, feedback, and support to the developers, testers, and project managers. The other options are not as effective or sufficient as including qualified application security personnel as part of the process, as they do not address the root cause of the lack of application hardening, which is the gap in skills and knowledge among the SDLC participants.

References: CDPSE Review Manual, 2021, p. 131

Asymmetric encryption is a method of encrypting and decrypting data using two different keys: a public key and a private key. The public key can be shared with anyone, while the private key is kept secret by the owner. Data encrypted with the public key can only be decrypted with the private key, and vice versa. Asymmetric encryption ensures the security of encryption keys when transferring data containing personal information between cloud applications, by providing the following benefits:

• It can prevent unauthorized access or use of the data, as only the intended recipient who has the matching private key can decrypt the data sent by the sender who has the public key.
• It can prevent man-in-the-middle attacks, where an attacker intercepts and modifies the data or keys in transit, as any tampering with the data or keys will result in decryption failure or error.
• It can enable digital signatures, where the sender encrypts a message digest of the data with their private key, and the recipient verifies it with the sender’s public key. Digital signatures can ensure the authenticity and integrity of the data and the sender.

The other options are less effective or irrelevant for ensuring the security of encryption keys when transferring data containing personal information between cloud applications. Whole disk encryption is a method of encrypting all the data on a disk or device, such as a laptop or a smartphone. It does not protect the data when they are transferred over a network or stored on a cloud server. Symmetric encryption is a method of encrypting and decrypting data using the same key. It requires both parties to securely exchange and store the key, which may be difficult or risky in a cloud environment. Digital signature is not a method of encryption, but an application of asymmetric encryption that can provide additional security features for data transmission.

Lawfulness and fairness is a data protection principle that states that personal data should be processed in a lawful, fair, and transparent manner in relation to the data subject. This means that personal data should be collected and used for legitimate purposes that are specified and communicated to the data subject, and that respect the rights and interests of the data subject. By posting its customer data protection notice that includes a statement indicating information is collected on how products are used, the content viewed, and the time and duration of online activities, an online business is applying the lawfulness and fairness principle. The online business is informing the customers about the purpose and scope of data collection, and obtaining their consent or legal basis for processing their personal data. References: : CDPSE Review Manual (Digital Version), page 2

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

Data anonymization is a method of protecting personal data by modifying or removing any information that can be used to identify an individual, either directly or indirectly, in a data set. Data anonymization aims to prevent the re-identification of the data subjects, even by the data controller or processor, or by using additional data sources or techniques. Data anonymization also helps to comply with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which require data controllers and processors to respect the privacy rights and preferences of the data subjects.

The data is transformed such that re-identification is impossible is an example of data anonymization, as it involves applying irreversible techniques, such as aggregation, generalization, perturbation, or synthesis, to alter the original data in a way that preserves their utility and meaning, but eliminates their identifiability. For example, a database of customer transactions can be anonymized by replacing the names and addresses of the customers with random codes, and by adding noise or rounding to the amounts and dates of the transactions.

The other options are not examples of data anonymization, but of other methods of protecting personal data that do not guarantee the impossibility of re-identification. The data is encrypted and a key is required to re-identify the data is an example of data pseudonymization, which is a method of replacing direct identifiers with pseudonyms, such as codes or tokens, that can be linked back to the original data with a key or algorithm. Data pseudonymization does not prevent re-identification by authorized parties who have access to the key or algorithm, or by unauthorized parties who can break or bypass the encryption. Key fields are hidden and unmasking is required to access to the data is an example of data masking, which is a method of concealing or obscuring sensitive data elements, such as names or credit card numbers, with characters, symbols or blanks. Data masking does not prevent re-identification by authorized parties who have permission to unmask the data, or by unauthorized parties who can infer or guess the hidden data from other sources or clues. Names and addresses are removed but the rest of the data is left untouched is an example of data deletion, which is a method of removing direct identifiers from a data set. Data deletion does not prevent re-identification by using indirect identifiers, such as age, gender, occupation or location, that can be combined or matched with other data sources to re-establish the identity of the data subjects.

References:

• Big Data Deidentification, Reidentification and Anonymization - ISACA, section 2: “Anonymization is the ability for the data controller to anonymize the data in a way that it is impossible for anyone to establish the identity of the data.”
• Data Anonymization - Overview, Techniques, Advantages, section 1: “Data anonymization is a method of ensuring that the company understands and enforces its duty to secure sensitive, personal, and confidential data in a world of highly complex data protection mandates that can vary depending on where the business and the customers are based.”

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

Sectoral is a privacy protection reference model that refers to a system of laws and regulations that apply to specific sectors or industries within a jurisdiction, such as health, finance, education or telecommunications. Sectoral privacy protection is typically characterized by having different rules and standards for different types of personal data or data processing activities, depending on the sensitivity and value of the data or the impact and risk of the processing. When a government’s health division established the complete privacy regulation for only the health market, it is using a sectoral privacy protection reference model, as it is addressing the specific needs and challenges of the health sector in terms of privacy protection. The other options are not applicable in this scenario. Co-regulatory is a privacy protection reference model that refers to a system of laws and regulations that are supplemented by self-regulation mechanisms, such as codes of conduct, standards or certification schemes, developed by industry associations or professional bodies with oversight from government agencies or regulators. Comprehensive is a privacy protection reference model that refers to a system of laws and regulations that apply to all sectors and industries within a jurisdiction, regardless of the type or nature of personal data or data processing activities. Self-regulatory is a privacy protection reference model that refers to a system of laws and regulations that rely on voluntary compliance by organizations with their own policies and procedures, without any external oversight or enforcement from government agencies or regulators1, p. 63-64 References: 1: CDPSE Review Manual (Digital Version)

A data inventory is a comprehensive list of the data that an organization collects, processes, stores, transfers, and disposes of. It includes information such as the type, source, location, owner, purpose, and retention period of the data. A data inventory is essential for understanding where personal data is coming from and how it is used within the organization, as well as for complying with data privacy laws and regulations. A data inventory also helps to identify and mitigate data privacy risks and gaps.

References:

• ISACA, CDPSE Review Manual 2021, Chapter 2: Privacy Governance, Section 2.2: Data Inventory and Data Mapping, p. 40-41.
• ISACA, Data Privacy Audit/Assurance Program, Control Objective 3: Data Inventory and Classification, p. 7-81