CDPSE Sample Questions Answers

Software hardening is a technique that mitigates design flaws in the application development process that may contribute to potential leakage of personal data. Software hardening is a process of modifying or configuring software to make it more secure and resilient against attacks or exploitation. Software hardening can involve various methods, such as removing unnecessary features or functions, disabling debugging or testing modes, applying patches or updates, implementing secure coding practices, etc. Software hardening helps to protect personal data by preventing or reducing the vulnerabilities that can allow unauthorized access, use, disclosure, or transfer of personal data. References: : CDPSE Review Manual (Digital Version), page 151

The best way to ensure data confidentiality across databases is to use data anonymization, which is a process of removing or modifying personal or sensitive data from a dataset so that it cannot be linked or attributed to a specific individual or entity. Data anonymization helps protect the privacy and security of the data subjects, as well as comply with the applicable data protection laws and regulations. Data anonymization can be achieved by using various techniques, such as masking, encryption, aggregation, generalization, perturbation, or synthetic data generation12.

References:

• CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.3 – Data Anonymization3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3 – Data Lifecycle, Section 3.4 – Data Anonymization4.

Beacons use Bluetooth low-energy (BLE) wireless technology to transmit information to nearby devices that have Bluetooth enabled. By disabling Bluetooth services on the mobile device, the user can prevent beacons from detecting and tracking their location and sending them unwanted messages or advertisements. This can help protect the user’s privacy and avoid potential security risks from malicious beacons. Disabling location services, enabling Trojan scanners, or enabling antivirus for mobile devices are not effective ways to address threats to mobile device privacy when using beacons as a tracking technology, because they do not prevent the communication between beacons and the mobile device.

References:

• Beacon Technology: What It Is and How It Impacts You1
• What Does It All Mean: Beacon Technology, GPS and Geofencing2

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.

References:

• Data Retention Policy 101: Best Practices, Examples & More - Intradyn
• Data retention - Wikipedia

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

Obtaining executive support is the first step in developing an organization-wide strategy to address data privacy risk, as it ensures that the privacy program has the necessary resources, authority, and alignment with the organization’s goals and objectives. Without executive support, the privacy program may face challenges in implementing and enforcing privacy policies, procedures, and controls across the organization. References: 2 Domain 1, Task 1

The value proposition of a PIA is not understood by management is the greatest obstacle to conducting a PIA, as it may result in lack of support, funding, resources or commitment for the PIA process and outcomes. Management may not appreciate or recognize the benefits of a PIA, such as enhancing privacy protection, reducing privacy risks and costs, increasing customer trust and satisfaction, and complying with privacy laws and regulations. Management may also perceive a PIA as a burden, a delay or a hindrance to the system or project development and delivery. The other options are not as significant as the value proposition of a PIA is not understood by management as obstacles to conducting a PIA. Conducting a PIA requires significant funding and resources is an obstacle to conducting a PIA, but it may be overcome by demonstrating the return on investment or the cost-benefit analysis of a PIA. PIAs need to be performed many times in a year is an obstacle to conducting a PIA, but it may be mitigated by adopting a scalable or modular approach to PIAs that can be tailored to different types or levels of systems or projects. The organization lacks knowledge of PIA methodology is an obstacle to conducting a PIA, but it may be resolved by acquiring or developing the necessary skills, tools or guidance for performing PIAs1, p. 67-68 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

Anonymization is a technique that removes or modifies all identifiers in a data set to prevent or limit the identification of the data subjects. Anonymization is the IT privacy practitioner’s best recommendation for an organization that uses analytics derived from archived transaction data to create individual customer profiles for customizing product and service offerings, as it would protect the privacy of the customers by reducing the linkability of the data set with their original identity, and also comply with the data minimization principle that requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. Anonymization would also preserve some characteristics or patterns of the original data that can be used for analysis or customization purposes, without compromising the accuracy or quality of the results. The other options are not as effective as anonymization in this situation. Discontinuing the creation of profiles is not a feasible or desirable option, as it would prevent the organization from achieving its business objectives and providing value to its customers. Implementing strong access controls is a security measure that restricts who can access, view or modify the data, but it does not address the issue of collecting or retaining more personal data than necessary or relevant. Encrypting data at rest is a security measure that transforms plain text data into cipher text using an algorithm and a key, making it unreadable by unauthorized parties, but it does not address the issue of collecting or retaining more personal data than necessary or relevant, and may require additional security measures to protect the encryption keys or certificates1, p. 75-76 References: 1: CDPSE Review Manual (Digital Version)

The first thing that an IT privacy practitioner should do before an organization migrates personal data from an on-premise solution to a cloud-hosted solution is to perform a privacy impact assessment (PIA). A PIA is a systematic process of identifying and evaluating the potential privacy risks and impacts of a data processing activity or system. A PIA helps to ensure that privacy is considered and integrated into the design and development of data processing activities or systems, and that privacy risks are mitigated or eliminated. A PIA also helps to determine the appropriate measures to protect personal data in a cloud-hosted solution, such as encryption, pseudonymization, anonymization, access control, audit trail, breach notification, etc. A PIA also helps to comply with the applicable privacy regulations and standards that govern data processing activities in a cloud-hosted solution. References: : CDPSE Review Manual (Digital Version), page 99

The primary reason for an organization to use hash functions when hardening application systems involved in biometric data processing is to reduce the risk of sensitive data breaches, because hash functions are one-way mathematical functions that transform biometric data into a unique and irreversible representation that cannot be reconstructed or reversed. This means that even if an attacker gains access to the hashed biometric data, they cannot use it to identify or impersonate the individual. Hash functions also help preserve the privacy and confidentiality of biometric data by preventing unauthorized access, modification, or disclosure.

References:

• CDPSE Exam Content Outline, Domain 2 – Privacy Architecture (Privacy Architecture Implementation), Task 2: Implement privacy solutions1.
• CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.3 – Privacy Architecture Implementation2.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 – Privacy Architecture, Section 2.4 – Remote Access3.

The most effective way to incorporate privacy by design principles into applications is to include privacy requirements in software development practices, because this ensures that privacy is considered and integrated from the early stages of the design process and throughout the entire lifecycle of the application. Software development practices include activities such as defining the scope, objectives, and specifications of the application, identifying and analyzing the privacy risks and impacts, selecting and implementing the appropriate privacy-enhancing technologies and controls, testing and validating the privacy functionality and performance, and monitoring and reviewing the privacy compliance and effectiveness of the application. By including privacy requirements in software development practices, the organization can achieve a proactive, preventive, and embedded approach to privacy that aligns with the privacy by design principles.

References:

• CDPSE Review Manual, 2023 Edition, Domain 2: Privacy Architecture, Section 2.1.2: Privacy Requirements, p. 75
• CDPSE Review Manual, 2023 Edition, Domain 2: Privacy Architecture, Section 2.2.1: Privacy by Design Methodology, p. 79-80
• The 7 Principles of Privacy by Design | Blog | OneTrust1

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)

Asymmetric encryption, also known as public-key encryption, encrypts and decrypts data using two separate yet mathematically connected cryptographic keys. One key is called the public key and can be shared with anyone, while the other key is called the private key and must be kept secret. The public key is used to encrypt the data, and only the corresponding private key can decrypt it. Likewise, the private key can be used to sign the data, and only the corresponding public key can verify it. This method provides confidentiality, integrity, authentication and non-repudiation for data.

References: CDPSE Review Manual, 2021, p. 117

Classifying sensitive unstructured data should be done first to address the situation of the proliferation of personal data held as unstructured data, 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. Classifying sensitive unstructured data also facilitates the data discovery, data minimization, data retention, and data disposal processes. References: 2 Domain 3, Task 2; 5 Page 9

The best way to safeguard the encryption keys is to ensure that they are stored in a cryptographic vault. A cryptographic vault is a secure hardware or software module that provides cryptographic services and protects the keys from unauthorized access, modification, or disclosure. A cryptographic vault can also provide other functions, such as key generation, key backup, key rotation, key destruction, and key auditing. A cryptographic vault can enhance the security and privacy of the encrypted data by preventing key compromise, leakage, or misuse. A cryptographic vault can also comply with the security standards and best practices for key management, such as the ISO/IEC 27002, NIST SP 800-57, or PCI DSS. References:

• [ISACA Glossary of Terms]
• [ISACA CDPSE Review Manual, Chapter 3, Section 3.3.3]
• [ISACA Journal, Volume 4, 2019, “Key Management in the Multi-Cloud Environment”]
• [ISACA CDPSE Review Manual, Chapter 3, Section 3.3.4]

De-identification is a technique that removes or modifies direct and indirect identifiers in a data set to prevent or limit the identification of the data subjects. De-identification reduces the risk of re-identification and thus limits the organization’s potential exposure in the event of consumer data loss. De-identification also maintains the traceability of the data by preserving some characteristics or patterns of the original data that can be used for analysis or research purposes. The other options are not effective ways to limit exposure and maintain traceability1, p. 75-76 References: 1: 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

Sensitive personal data is a subset of personal data that reveals or relates to more intimate or confidential aspects of a person’s identity, such as their racial or ethnic origin, religious or philosophical beliefs, health status, sexual orientation, political opinions, trade union membership, biometric or genetic data, or criminal record. Sensitive personal data is subject to more stringent legal and regulatory protections and requires a higher level of consent from the data subject to be processed. Mailing address, bank account login ID, and contact phone number are examples of personal data, but not sensitive personal data, as they do not reveal or relate to such intimate or confidential aspects of a person’s identity.

References: CDPSE Review Manual, 2021, p. 29

The best control to prevent data leakage for a cloud-based HR solution with a mobile application interface is to disable the download of data to the mobile devices. This is because downloading data to the mobile devices increases the risk of data loss, theft, or unauthorized access, especially if the devices are lost, stolen, or compromised. Disabling the download of data to the mobile devices ensures that the data remains in the cloud-based solution, where it can be protected by encryption, access control, and other security measures. The other options are not as effective or sufficient as disabling the download of data to the mobile devices, as they do not address the root cause of the data leakage risk, which is the exposure of data outside the cloud-based solution.

References: CDPSE Review Manual, 2021, p. 128

The primary means by which an organization communicates customer rights as it relates to the use of their personal information is publishing a privacy notice. A privacy notice is a document that informs the customers about how the organization collects, uses, shares, and protects their personal information, and what rights and choices they have regarding their data4. A privacy notice is a legal requirement under many data protection laws and regulations, such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), or the Personal Information Protection and Electronic Documents Act (PIPEDA)5 . A privacy notice is also a good practice to demonstrate the organization’s commitment to transparency, accountability, and customer trust. References:

• ISACA Glossary of Terms
• Article 13 and 14 of the GDPR
• [Section 1798.100 of the CCPA]
• [Schedule 1, Principle 4.8 of the PIPEDA]
• [ISACA CDPSE Review Manual, Chapter 1, Section 1.3.2]

Encryption of network traffic is the most important control to mitigate the risk of potential personal data compromise when employees access corporate IT assets remotely. Encryption is a process that transforms data into an unreadable form, making it difficult for unauthorized parties to intercept, modify, or steal it. Encryption of network traffic ensures that the data transmitted between the remote employees and the corporate network is protected from eavesdropping, tampering, or leakage.

Intrusion prevention system (IPS), firewall rules review, and intrusion detection system (IDS) are also useful controls for network security, but they are not as effective as encryption for protecting personal data in transit. IPS and IDS can monitor and block malicious or suspicious network traffic, but they cannot prevent data exposure if the traffic is intercepted by a third party. Firewall rules review can help optimize and secure the firewall configuration, but it cannot guarantee that the firewall will not be bypassed or compromised by an attacker. Therefore, encryption of network traffic is the best option among the choices given.

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.

Requiring independent audits of the providers’ data privacy controls is the best way to ensure third-party providers that process an organization’s personal data are addressed as part of the data privacy strategy. Independent audits can verify that the providers are complying with the applicable data privacy laws and regulations, as well as the organization’s own policies and standards. Independent audits can also identify any gaps or weaknesses in the providers’ data privacy controls and recommend corrective actions or improvements.

References:

• What Is Your Privacy and Data Protection Strategy? - ISACA
• Why data privacy and third-party risk teams need to work together - OneTrust

One of the key principles of data protection is transparency, which means that individuals have the right to be informed about the collection and use of their personal data. This applies to video surveillance as well, especially in high security areas where the impact on privacy may be significant. Therefore, it is important to inform those affected by video surveillance about the purpose, scope, retention and access policies of the data collected.

References:

• ISACA Certified Data Privacy Solutions Engineer (CDPSE) Exam Content Outline, Domain 2: Privacy Architecture, Task 2.1: Design privacy controls based on privacy principles and legal requirements, Subtask 2.1.1: Identify applicable privacy principles and legal requirements.
• How can we comply with the data protection principles when using surveillance systems? | ICO

A private cloud is a cloud deployment model that provides exclusive access and control to a single organization or a specific group of users within the organization. A private cloud is best for an organization whose main objectives are to logically isolate personal data from other tenants and adopt custom privacy controls for the data, as it offers the highest level of security, privacy, and customization among the cloud deployment models. A private cloud allows the organization to implement its own privacy policies, standards, and procedures for the personal data, as well as to configure the cloud infrastructure, services, and applications according to its specific needs and preferences. A private cloud also reduces the risk of data breaches, unauthorized access, or co-mingling of data from other tenants, as the personal data is stored and processed in a dedicated and isolated environment.

References: CDPSE Review Manual, 2021, p. 125

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)

A mobile application implementation should meet the organization’s data security standards by ensuring that the application does not contain any vulnerabilities, errors or malicious code that could compromise the confidentiality, integrity or availability of the data. An automatic dynamic code scan is a technique that analyzes the application code while it is running to detect and report any security issues or defects. An automatic dynamic code scan can help to identify and fix any potential data security risks before the application is deployed. The other options are not sufficient to ensure data security standards. UAT is a process of verifying that the application meets the user requirements and expectations, but it does not necessarily test for data security. Data classification is a process of categorizing data according to its sensitivity and value, but it does not ensure that the data is protected by the application. A PIA is a process of identifying and evaluating the privacy impacts of a system or project that involves personal data, but it does not ensure that the system or project meets data security standards. , p. 89-90 References: : CDPSE Review Manual (Digital Version)

The first consideration when selecting a data sanitization method is the type of storage device that holds the data to be sanitized. Different types of storage devices have different characteristics and limitations that affect the effectiveness and feasibility of data sanitization methods. For example, magnetic media, such as hard disk drives (HDDs), can be sanitized by data degaussing, which is wiping data permanently by weakening the magnetic field1. However, data degaussing is not applicable to devices that use solid state drive (SSD) technology, since SSDs do not store data magnetically2. Therefore, the storage type determines which data sanitization methods are suitable and available for the data disposal process.

References:

• ISACA, Why (and How to) Dispose of Digital Data, Data Degaussing1
• ISACA, Best Practices for Data Hygiene, Data Hygiene Practices3
• TechReset, Data Sanitization and Methods, Cryptographic Erasure2
• Imperva, What is Data Sanitization?4

The first step when performing a data quality assessment is to assess the completeness of the data inventory, which is a comprehensive list of all data assets within the organization. This will help identify the scope, sources, owners, and characteristics of the data to be assessed. The other options are possible actions that may be taken after the data inventory is complete, depending on the objectives and criteria of the assessment.

References:

• CDPSE Exam Content Outline, Domain 3 – Data Lifecycle (Data Quality), Task 1: Perform a data quality assessment1.
• CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.2 – Data Quality2.

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

Authentication is a process of verifying the identity of a user or device that requests access to a system or resource. Authentication can be based on one or more factors, such as something the user knows (e.g., password), something the user has (e.g., token), something the user is (e.g., fingerprint) or something the user does (e.g., signature). When an organization requires a photo on a government-issued identification card to validate an in-person credit card purchase, it is using possession factor authentication, which relies on something the user has as proof of identity. The other options are not applicable in this scenario1, p. 81 References: 1: CDPSE Review Manual (Digital Version)

A bug bounty program is an assurance approach that involves offering rewards to external security researchers who find and report vulnerabilities in an API or other software. A bug bounty program can be more effective than other assurance approaches in identifying API vulnerabilities because it leverages the skills, creativity, and diversity of a large pool of ethical hackers who can test the API from different perspectives and scenarios. A bug bounty program can also incentivize continuous testing and reporting of vulnerabilities, which can help improve the security posture of the API over time.

References:

• 10 top API security testing tools, CSO Online
• Bug Bounty Programs: What You Need to Know, ISACA Journal

Data mapping is the process of defining how data elements from different sources are related, transformed, and transferred to a common destination. Data mapping is the first step when developing an application link because it helps to ensure that the data exchanged between the API and the third-party application is consistent, accurate, and compatible. Data mapping also helps to identify any gaps, errors, or conflicts in the data and resolve them before the data transfer occurs.

References:

• What is Data Mapping?, Talend
• Data Mapping: What It Is and How to Do It, Xplenty

The first consideration when conducting a privacy impact assessment (PIA) is the applicable privacy legislation that governs the collection, processing, storage, transfer, and disposal of personal data within the scope of the assessment. The applicable privacy legislation may vary depending on the jurisdiction, sector, or purpose of the data processing activity. The PIA should identify and comply with the relevant legal requirements and obligations for data protection and privacy, such as obtaining consent, providing notice, ensuring data quality and security, respecting data subject rights, and reporting data breaches. The applicable privacy legislation also determines the criteria, methodology, and documentation for conducting the PIA.

References:

• ISACA, Performing an Information Security and Privacy Risk Assessment1
• ISACA, Best Practices for Privacy Audits2
• ISACA, GDPR Data Protection Impact Assessments3
• ISACA, GDPR Data Protection Impact Assessment Template4

Requirements definition is a phase of the software development life cycle (SDLC) that involves gathering, analyzing and documenting the functional and non-functional requirements of the software system or application, such as features, performance, security and usability. It is most important to consider privacy by design principles during this phase, as it would help to ensure that privacy is embedded and integrated into the software system or application from the outset, rather than as an afterthought or an add-on. Considering privacy by design principles during requirements definition would also help to avoid costly rework or delays later in the SDLC, as well as to enhance customer trust and satisfaction, and comply with privacy laws and regulations. The other options are not as important as requirements definition in considering privacy by design principles. Application design is a phase of the SDLC that involves creating and specifying the architecture, components, interfaces and data models of the software system or application, based on the requirements defined in the previous phase. Implementation is a phase of the SDLC that involves coding, testing and debugging the software system or application, based on the design specifications created in the previous phase. Testing is a phase of the SDLC that involves verifying and validating that the software system or application meets the requirements and expectations of the users and stakeholders, as well as identifying and fixing any defects or errors1, p. 88-89 References: 1: CDPSE Review Manual (Digital Version)

The applicable privacy legislation needs to be identified first to define the privacy requirements to use when assessing the selection of IT systems, because it sets the legal obligations and standards for the organization to comply with when processing personal data. The type of data, the control frameworks, and the technology platforms are all dependent on the privacy legislation that applies to the organization and its data processing activities. Therefore, the privacy legislation is the primary source of privacy requirements for IT systems.

References:

• CDPSE Review Manual, 2023 Edition, Domain 2: Privacy Architecture, Section 2.1.2: Privacy Requirements, p. 75
• Compliance with Cybersecurity and Privacy Laws and Regulations1

Attribute-based access control (ABAC) is the best approach for limiting the access of regional HR team members to employee data only within their regional office, because it allows for fine-grained and dynamic access control based on attributes of the subject, object, environment, and action. Attributes are characteristics or properties that can be used to describe or identify entities, such as users, resources, locations, roles, or permissions. ABAC uses policies and rules that evaluate the attributes and grant or deny access accordingly. For example, an ABAC policy could state that a user can access an employee record if and only if the user’s role is HR and the user’s region matches the employee’s region. This way, the access control can be tailored to the specific needs and context of the organization, without relying on predefined or fixed access levels.

References:

• Attribute-Based Access Control (ABAC), NIST
• What is Attribute-Based Access Control (ABAC)?, Axiomatics
• Access Control Models – Westoahu Cybersecurity, Westoahu Cybersecurity

One of the most common vulnerabilities that can compromise the access to personal information is end users using weak passwords. Weak passwords are passwords that are easy to guess, crack, or steal, such as passwords that are short, simple, common, or reused. Weak passwords can allow unauthorized or malicious parties to gain access to personal information and cause privacy breaches, leaks, or misuse. Multi-factor authentication is an effective way to mitigate this vulnerability, as it requires end users to provide more than one piece of evidence to verify their identity, such as something they know (e.g., password), something they have (e.g., token), or something they are (e.g., biometric). Multi-factor authentication makes it harder for attackers to bypass the authentication process and access personal information. References: : CDPSE Review Manual (Digital Version), page 107

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.”

Height, weight, and activities are the most legitimate information to collect for business reasons in this situation, as they are directly related to the purpose and functionality of a wellness smartwatch application that aims to monitor and improve the health and fitness of its users. Collecting height, weight, and activities would also comply with the data minimization principle that requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. The other options are not legitimate information to collect for business reasons in this situation, as they are not related to the purpose and functionality of a wellness smartwatch application and may violate the privacy rights and preferences of its users. Collecting sleep schedule and calorie intake may be useful for some users who want to track their sleep quality and nutrition intake, but they are not essential for a wellness smartwatch application and may require additional consent or justification from the users. Collecting education and profession may be irrelevant for a wellness smartwatch application and may be used for other purposes, such as marketing or profiling, without the consent or knowledge of the users. Collecting race, age, and gender may be sensitive for some users who do not want to disclose their personal characteristics or identity, and may require additional safeguards or measures to protect their privacy1, p. 75-76 References: 1: CDPSE Review Manual (Digital Version)

Extended detection and response (XDR) is a security solution that collects and analyzes data from multiple sources, such as endpoints, networks, servers, cloud, and applications, to detect and respond to threats in real time. XDR should be installed to address the increase in threats originating from endpoints, as it provides a holistic and integrated view of the threat landscape, as well as automated and coordinated actions to contain and remediate the threats. XDR also helps to improve the visibility, efficiency, and effectiveness of the security operations, as well as to reduce the complexity and costs of managing multiple security tools.

References: CDPSE Review Manual, 2021, p. 149

The primary reason to complete a privacy impact assessment (PIA) is to understand privacy risks associated with the collection, use, disclosure or retention of personal data. A PIA is a systematic process to identify and evaluate the potential privacy impacts of a system, project, program or initiative that involves personal data processing activities. A PIA helps to ensure that privacy risks are identified and mitigated before the implementation is executed. A PIA also helps to ensure compliance with privacy principles, laws and regulations, and alignment with customer expectations and preferences. The other options are not primary reasons to complete a PIA. To comply with consumer regulatory requirements may be a reason to complete a PIA, but it is not the primary reason, as consumer regulatory requirements may vary depending on the context and jurisdiction. To establish privacy breach response procedures may be an outcome of completing a PIA, but it is not the primary reason, as privacy breach response procedures are only one aspect of mitigating privacy risks. To classify personal data may be an activity that is part of completing a PIA, but it is not the primary reason, as personal data classification is only one aspect of understanding privacy risks1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

 Irreversible disposal is a process of removing or destroying data from a storage device or media to prevent unauthorized access or recovery of the data. Irreversible disposal is the most important thing to establish within a data storage policy to protect data privacy, as it reflects the principles of data minimization and storage limitation, which require limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes, and deleting or disposing of personal data when it is no longer needed or justified. Irreversible disposal also helps to reduce the privacy risks and costs associated with data storage and retention, such as data breaches, unauthorized access, misuse or loss of data. The other options are not as important as irreversible disposal in protecting data privacy within a data storage policy. Data redaction is a technique that removes or obscures sensitive or confidential information from a document or file, but it does not address the issue of data retention or deletion. Data quality assurance (QA) is a process of ensuring that the data meets the standards and specifications of accuracy, completeness, consistency and reliability, but it does not address the issue of data retention or deletion. Collection limitation is a principle that requires limiting the collection of personal data to what is necessary and relevant for the intended purposes, but it does not address the issue of data retention or deletion1, p. 75-76 References: 1: CDPSE Review Manual (Digital Version)

The best answer is D. Mutual certificate authentication.

A comprehensive explanation is:

Mutual certificate authentication is a method of mutual authentication that uses public key certificates to verify the identities of both parties in a two-way communication. A public key certificate is a digital document that contains information about the identity of the certificate holder, such as their name, organization, domain name, etc., as well as their public key, which is used for encryption and digital signature. A public key certificate is issued and signed by a trusted authority, called a certificate authority (CA), that vouches for the validity of the certificate.

Mutual certificate authentication works as follows:

• Both parties have a public key certificate issued by a CA that they trust.
• When they initiate a communication, they exchange their certificates with each other.
• They verify the signatures on the certificates using the CA’s public key, which they already have or can obtain from a trusted source.
• They check that the certificates are not expired, revoked, or tampered with.
• They extract the public keys from the certificates and use them to encrypt and decrypt messages or to generate and verify digital signatures.
• They confirm that the identities in the certificates match their expectations and intentions.

By using mutual certificate authentication, both parties can be confident that they are communicating with the intended and legitimate party, and that their communication is secure and confidential.

Mutual certificate authentication is often used in conjunction with Transport Layer Security (TLS), a protocol that provides encryption and authentication for network communications. TLS supports both one-way and two-way authentication. In one-way authentication, only the server presents a certificate to the client, and the client verifies it. In two-way authentication, also known as mutual TLS or mTLS, both the server and the client present certificates to each other, and they both verify them. Mutual TLS is commonly used for secure web services, such as APIs or webhooks, that require both parties to authenticate each other.

Virtual private network (VPN), Secure Shell (SSH), and Transport Layer Security (TLS) are all technologies that can help to ensure the identities of individuals in a two-way communication are verified, but they are not methods of mutual authentication by themselves. They can use mutual certificate authentication as one of their options, but they can also use other methods, such as username and password, pre-shared keys, or tokens. Therefore, they are not as specific or accurate as mutual certificate authentication.

References:

• What is mutual authentication? | Two-way authentication1
• How to prove and verify someone’s identity2
• Identity verification - Information Security & Policy3

The answer is B. Personal data could potentially be exfiltrated through the virtual workspace.

A comprehensive explanation is:

A virtualized workspace is a cloud-based service that provides remote access to a desktop environment, applications, and data. A virtualized workspace can enable software development teams to collaborate and work efficiently across different locations and devices. However, a virtualized workspace also poses significant privacy risks, especially when it is implemented by a third-party provider.

One of the greatest privacy concerns of using a third-party virtualized workspace is the potential for personal data to be exfiltrated through the virtual workspace. Personal data is any information that relates to an identified or identifiable individual, such as name, email, address, phone number, etc. Personal data can be collected, stored, processed, or transmitted by the software development organization or its clients, partners, or users. Personal data can also be generated or inferred by the software development activities or products.

Personal data can be exfiltrated through the virtual workspace by various means, such as:

• Data breaches: A data breach is an unauthorized or unlawful access to or disclosure of personal data. A data breach can occur due to weak security measures, misconfiguration errors, human errors, malicious attacks, or insider threats. A data breach can expose personal data to hackers, competitors, regulators, or other parties who may use it for harmful purposes.
• Data leakage: Data leakage is an unintentional or accidental transfer of personal data outside the intended boundaries of the organization or the virtual workspace. Data leakage can occur due to improper disposal of devices or media, insecure network connections, unencrypted data transfers, unauthorized file sharing, or careless user behavior. Data leakage can compromise personal data to third parties who may not have adequate privacy policies or practices.
• Data mining: Data mining is the analysis of large and complex data sets to discover patterns, trends, or insights. Data mining can be performed by the third-party provider of the virtual workspace or by other authorized or unauthorized parties who have access to the virtual workspace. Data mining can reveal personal data that was not explicitly provided or intended by the organization or the individuals.

The exfiltration of personal data through the virtual workspace can have serious consequences for the software development organization and its stakeholders. It can result in:

• Legal liability: The organization may face legal actions or penalties for violating the privacy laws, regulations, standards, or contracts that apply to the personal data in each jurisdiction where it operates or serves. For example, the General Data Protection Regulation (GDPR) in the European Union imposes strict obligations and sanctions for protecting personal data across borders.
• Reputational damage: The organization may lose trust and credibility among its clients, partners, users, employees, investors, or regulators for failing to safeguard personal data. This can affect its brand image, customer loyalty, market share, revenue, or growth potential.
• Competitive disadvantage: The organization may lose its competitive edge or intellectual property if its personal data is stolen or misused by its rivals or adversaries. This can affect its innovation capability, product quality, or market differentiation.

Therefore, it is essential for the software development organization to implement appropriate measures and controls to prevent or mitigate the exfiltration of personal data through the virtual workspace. Some of these measures and controls are:

• Data minimization: The organization should collect and process only the minimum amount and type of personal data that is necessary and relevant for its legitimate purposes. It should also delete or anonymize personal data when it is no longer needed or required.
• Data encryption: The organization should encrypt personal data at rest and in transit using strong and standardized algorithms and keys. It should also ensure that only authorized parties have access to the keys and that they are stored securely.
• Data segmentation: The organization should segregate personal data into different categories based on their sensitivity and risk level. It should also apply different levels of protection and access control to each category of personal data.
• Data governance: The organization should establish a clear and comprehensive policy and framework for managing personal data throughout its lifecycle. It should also assign roles and responsibilities for implementing and enforcing the policy and framework.
• Data audit: The organization should monitor and review the activities and events related to personal data on a regular basis. It should also conduct periodic assessments and tests to evaluate the effectiveness and compliance of its privacy measures and controls.
• Data awareness: The organization should educate and train its staff and users on the importance and best practices of protecting personal data. It should also communicate and inform its clients, partners, and regulators about its privacy policies and practices.

The other options are not as great of a concern as option B.

The third-party workspace being hosted in a highly regulated jurisdiction (A) may pose some challenges for complying with different privacy laws and regulations across borders. However it may also offer some benefits such as higher standards of privacy protection and enforcement.

The organization’s products being classified as intellectual property © may increase the value and attractiveness of the personal data related to the products, but it does not necessarily increase the risk of exfiltration of the personal data through the virtual workspace.

The lack of privacy awareness and training among remote personnel (D) may increase the likelihood of human errors or negligence that could lead to exfiltration of personal data through the virtual workspace. However it is not a direct cause or source of exfiltration, and it can be addressed by providing adequate education and training.

References:

• 8 Risks of Virtualization: Virtualization Security Issues1
• Security & Privacy Risks of the Hybrid Work Environment2
• The Risk of Virtualization - Concerns and Controls3
• What is Virtualized Security?4

The best reason for a health organization to use desktop virtualization to implement stronger access control to systems containing patient records is that it can improve data integrity and reduce effort for privacy audits. Desktop virtualization is a technology that allows users to access a virtual desktop environment that is hosted on a remote server, rather than on their local device. Desktop virtualization can enhance data privacy by providing stronger access control to systems containing patient records, such as requiring authentication, authorization, encryption, logging, etc. Desktop virtualization can also improve data integrity by ensuring that patient records are stored and processed in a centralized and secure location, rather than on multiple devices that may be vulnerable to loss, theft, damage, or corruption. Desktop virtualization can also reduce effort for privacy audits by simplifying the management and monitoring of data privacy compliance across different devices and locations. References: : CDPSE Review Manual (Digital Version), page 153

Any organization collecting information about EU residents is required to operate with transparency in collecting and using their personal information. Chapter III of the GDPR defines eight data subject rights that have become foundational for other privacy regulations around the world:

Right to access personal data. Data subjects can access the data collected on them.

One of the privacy requirements related to the rights of data subjects is the right to access, which means that individuals have the right to obtain a copy of their personal data, as well as information about how their data is processed, by whom, for what purposes, and for how long. To meet this requirement, an organization’s technology stack should incorporate features that allow individuals to have direct access to their data, such as self-service portals, dashboards, or applications. This way, individuals can exercise their right to access without relying on intermediaries or manual processes, which can be inefficient, error-prone, or insecure. References: : CDPSE Review Manual (Digital Version), page 137

The right not to be profiled is the right of data subjects to not be subject to a decision based solely on automated processing, including profiling, which produces legal or similarly significant effects on them. The online application that delivers a mortgage decision automatically based on criteria set by the lender is an example of such a decision, as it affects the data subject’s ability to obtain a loan.

References:

• What exactly is ‘profiling’ under the GDPR - DMA
• Can I be subject to automated individual decision-making, including profiling - European Commission

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.

References:

• CDPSE Review Manual (Digital Version), Domain 2: Privacy Architecture, Task 2.4: Design and/or implement privacy controls1
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3: Privacy Architecture, Section: Privacy Controls2

Prioritizing privacy-related risk scenarios as part of ERM processes is the best way to ensure that the risk responses meet the organizational objectives, because it helps to align the privacy risk management with the overall strategic goals, values, and culture of the organization. ERM is a holistic approach to identify, assess, and manage risks across the organization, taking into account the interdependencies and trade-offs among different types of risks. By integrating privacy-related risk scenarios into the ERM processes, the organization can evaluate the potential impact and likelihood of privacy risks on its mission, vision, and performance, and prioritize the most significant ones for mitigation or acceptance. This can also help to allocate appropriate resources, assign clear roles and responsibilities, and monitor and report on the effectiveness of the risk responses.

References:

• Privacy Risk Management, ISACA Journal
• Enterprise Risk Assessment, Deloitte

Compartmentalizing resource access is a security technique that divides a system or network into separate segments or zones with different levels of access and control, based on the sensitivity and value of the data or resources. Compartmentalizing resource access would most effectively reduce the impact of a successful breach through a remote access solution, as it would limit the scope and extent of the breach, and prevent unauthorized access to other segments or zones that contain more critical or sensitive data or resources. The other options are not as effective as compartmentalizing resource access in reducing the impact of a successful breach through a remote access solution. Regular testing of system backups is a security technique that verifies the availability and recoverability of data in case of a system failure or disaster, but it does not prevent or limit unauthorized access to data. Monitoring and reviewing remote access logs is a security technique that records and analyzes the activities and events related to remote access sessions, but it does not prevent or limit unauthorized access to data. Regular physical and remote testing of the incident response plan is a security technique that evaluates and improves the readiness and effectiveness of an organization’s response to security incidents, but it does not prevent or limit unauthorized access to data1, p. 91-92 References: 1: CDPSE Review Manual (Digital Version)

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.”

Validation and certification of data destruction is the most important consideration when choosing a method for data destruction, because it provides evidence that the data has been destroyed beyond recovery and that the organization has complied with the applicable information security frameworks and legal requirements. Validation and certification can also help to prevent data breaches, avoid legal liabilities, and enhance the organization’s reputation and trustworthiness. Different methods of data destruction may have different levels of validation and certification, depending on the type of media, the sensitivity of the data, and the standards and guidelines followed. For example, some methods may require a third-party verification or audit, while others may generate a certificate of destruction or a report of erasure. Therefore, the organization should choose a method that can provide sufficient validation and certification for its specific needs and obligations.

References:

• Secure Data Disposal and Destruction: 6 Methods to Follow, KirkpatrickPrice
• Data Destruction Standards and Guidelines, BitRaser
• Best Practices for Data Destruction, U.S. Department of Education

After a privacy risk has been accepted, the next step is to monitor the risk landscape for material changes. This means that the organization should keep track of any internal or external factors that may affect the likelihood or impact of the risk, such as new threats, vulnerabilities, regulations, technologies, or business processes. Monitoring the risk landscape can help the organization identify if the risk acceptance decision is still valid, or if it needs to be revisited or revised. Monitoring can also help the organization prepare for potential incidents or consequences that may arise from the accepted risk.

The notice provided to customers during data collection is the most important consideration when determining retention periods for personal data, as it reflects the transparency and accountability principles of privacy and the expectations and preferences of the data subjects. The notice should inform the customers about the purposes and legal bases of the data processing, the rights and choices of the customers, and the safeguards and measures to protect the data, including how long the data will be kept and when it will be deleted or disposed of. The notice should also be consistent with the applicable laws and regulations that may prescribe or limit the retention periods for certain types of personal data. The other options are not as important as the notice provided to customers during data collection when determining retention periods for personal data. Sectoral best practices for the industry may provide some guidance or benchmarks for retention periods, but they may not reflect the specific context or needs of the organization or the customers. Data classification standards may help to categorize data according to its sensitivity and value, but they may not indicate how long the data should be retained or deleted. Storage capacity available for retained data may affect the feasibility or cost of retaining data, but it should not determine or override the retention periods based on privacy principles, laws or customer expectations1, p. 99-100 References: 1: CDPSE Review Manual (Digital Version)