CDPSE Certified Data Privacy Solutions Engineer Questions and Answers

Automated data discovery and classification tools provide continuous and systematic identification of data assets across the enterprise, ensuring accuracy and reducing manual errors. Internal updates (B) and departmental reporting (D) depend on human diligence; periodic manual audits (C) are time-bound and may miss changes between reviews.

“Automated discovery ensures a current and reliable data inventory, critical for privacy compliance.”

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.

Degaussing is a hard drive sanitation method that uses a powerful magnetic field to erase or destroy the data stored on a magnetic disk or tape. Degaussing should be used to sanitize hard drives containing personal data prior to being crushed, as it provides an additional layer of assurance that data has been permanently erased and cannot be recovered by any means. Degaussing also damages the drive itself, making it unusable for future storage. The other options are not effective or necessary hard drive sanitation methods prior to being crushed. Low-level formatting is a hard drive sanitation method that erases the data and the partition table on the drive, but it may leave some traces of data that can be recovered by forensic tools or software. Remote partitioning is a hard drive sanitation method that creates separate logical sections on the drive, but it does not erase or destroy the data on the drive. Hammer strike is a hard drive sanitation method that physically damages the drive by hitting it with a hammer, but it may not erase or destroy the data completely or prevent data recovery by advanced tools or techniques1, p. 93-94 References: 1: CDPSE Review Manual (Digital Version)

Senior leadership must define the roles and responsibilities of the person with oversight, who is responsible for ensuring compliance with the data privacy policy and applicable laws and regulations. This person may also be known as the data protection officer, the privacy officer, or the chief privacy officer, depending on the organization and jurisdiction. The person with oversight should have the authority, resources, and independence to perform their duties effectively.

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.

A current and accurate data map enables organizations to locate personal data across systems, which is essential for responding to access, rectification, erasure, and portability requests. DLP (A) prevents leakage, not rights fulfillment; breach handling (B) addresses incidents, not rights; contact forms (C) provide intake but not fulfillment.

“Data maps provide visibility into where and how personal data is processed, enabling rights fulfillment.”

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)

Biometric records are personal information that can be used to identify an individual based on their physical or behavioral characteristics, such as fingerprints, facial recognition, iris scans, voice patterns, etc. Biometric records are considered sensitive personal information that require special protection and consent from the data subject. Biometric records can be used for various purposes, such as authentication, identification, security, etc., but they also pose privacy risks, such as unauthorized access, use, disclosure, or transfer of biometric data. References: : CDPSE Review Manual (Digital Version), page 25

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 greatest privacy risk for client-side application processing is an employee loading personal information on a company laptop. Client-side application processing refers to performing data processing operations on the user’s device or browser, rather than on a server or cloud. This can improve performance and user experience, but also pose privacy risks if the user’s device is lost, stolen, hacked, or infected with malware. An employee loading personal information on a company laptop is exposing that information to potential threats on the client-side, such as unauthorized access, use, disclosure, modification, or loss. Therefore, an organization should implement appropriate security measures to protect personal information on client-side devices, such as encryption, authentication, authorization, logging, monitoring, etc. References: : CDPSE Review Manual (Digital Version), page 153

A data dictionary is a document that defines and describes the data elements, attributes, formats, sources, destinations, purposes and relationships of a data set or system. A data dictionary would be the best way to ensure personal data usage is standardized across the entire organization, as it would provide a common and consistent understanding and reference for how personal data is collected, used, disclosed and transferred within and outside the organization. A data dictionary would also help to ensure compliance with privacy principles, such as accuracy, transparency and accountability. The other options are not as effective as developing a data dictionary in ensuring personal data usage is standardized across the entire organization. De-identify all data 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, but it does not ensure standardization or consistency of personal data usage across the organization. Encrypt all sensitive data is a technique that transforms plain text data into cipher text using an algorithm and a key, making it unreadable by unauthorized parties, but it does not ensure standardization or consistency of personal data usage across the organization. Perform data discovery is a process of identifying and locating personal data within an organization’s systems, databases, applications or files, but it does not ensure standardization or consistency of personal data usage across the organization1, p. 69-70 References: 1: CDPSE Review Manual (Digital Version)

Collecting more data than necessary for recruitment violates the principle of data minimization, which requires limiting collection to what is adequate, relevant, and necessary. Transparency (A) deals with notice, localization (B) concerns storage jurisdiction, and quality (D) addresses accuracy.

“Data minimization: collect and process only the data that is necessary for the stated purpose.”

Incorporating privacy checkpoints into the secure development life cycle (SDLC) is the best way to avoid collecting personal data that was not part of the privacy impact assessment (PIA). Privacy checkpoints are stages in the SDLC where privacy requirements and risks are reviewed and validated, and any changes or deviations from the original PIA are identified and addressed. Privacy checkpoints help ensure that privacy is embedded throughout the system design and development, and that any changes are documented and approved.

According to the General Data Protection Regulation (GDPR), data subjects have the right to withdraw their consent for processing their personal data at any time. However, this right does not apply when the processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes, in accordance with Article 89(1) of the GDPR1.

The principle of data minimization states that personal data should be adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed. By using only the data required by the application, the organization can reduce the amount of data that is collected, stored, processed and potentially exposed. This can also help the organization comply with privacy laws and regulations that require data minimization, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

Transport Layer Security (TLS) is a protocol that provides secure communication over the internet by encrypting and authenticating data. TLS provides data integrity through the calculation of message digests, which are cryptographic hashes that summarize the content and structure of a message. The sender and the receiver of a message can compare the message digests to verify that the message has not been altered or corrupted during transmission. TLS also uses digital certificates, asymmetric encryption, and symmetric encryption to provide confidentiality and authentication, but these are not directly related to data integrity.

Conducting a risk assessment of all candidate vendors is the best way to provide assurance that a potential vendor is able to comply with privacy regulations and the organization’s data privacy policy, because it allows the organization to evaluate the vendor’s privacy practices, controls, and performance against a set of criteria and standards. A risk assessment can also help to identify any gaps, weaknesses, or threats that may pose a risk to the organization’s data privacy objectives and obligations. A risk assessment can be based on various sources of information, such as self-attestations, documentation, audits, or independent verification. A risk assessment can also help to prioritize the vendors based on their level of risk and impact, and to determine the appropriate mitigation or monitoring actions.

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 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)

The need-to-know basis principle is a security principle that states that access to personal data should be limited to those who have a legitimate purpose for accessing it. The need-to-know basis principle helps to protect data privacy by minimizing the exposure of personal data to unauthorized or unnecessary parties, reducing the risk of data breaches, leaks, or misuse. The need-to-know basis principle should be applied when designing a role-based user access model for a new application, by defining clear roles and responsibilities for different users, granting access rights based on their roles and functions, and enforcing access controls and audits to monitor and verify data access. References: : CDPSE Review Manual (Digital Version), page 105

The use of personal data in model training is the primary privacy risk with LLMs, since once trained, models may retain, reproduce, or infer personal data without proper controls. Hallucinations (B), expertise shortages (C), and interoperability issues (D) are operational or performance risks, but not privacy risks.

“Training models on personal data can result in unintended retention, exposure, or disclosure of sensitive information.”

A privacy impact assessment (PIA) is a process of analyzing the potential privacy risks and impacts of collecting, using, and disclosing personal data. A PIA should be conducted when there is a change in the data processing activities that may affect the privacy of individuals or the compliance with data protection laws and regulations. One of the scenarios that should trigger the completion of a PIA is when there are new inter-organizational data flows, which means that personal data is shared or transferred between different entities or jurisdictions. This may introduce new privacy risks, such as unauthorized access, misuse, or breach of data, as well as new legal obligations, such as obtaining consent, ensuring adequate safeguards, or notifying authorities.

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

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 step for an IT privacy practitioner following a decision to expand remote working capability is to evaluate the impact resulting from this change on the organization’s privacy policies, programs and practices. This will help identify the risks and gaps that need to be addressed, as well as the opportunities for improvement and optimization. The other options are possible actions that may be taken after the impact assessment, depending on the results and recommendations.

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.

The data controller determines the purposes and means of processing and therefore retains primary accountability for risk—even if a processor or custodian executes processing. Data processors (C) act under instructions; custodians (A) safeguard data but do not define processing; data scientists (B) are operational users, not accountable owners.

“The data controller remains accountable for compliance and risk regardless of outsourcing processing activities.”

Consent tagging is a metadata-driven process that associates consent preferences with an individual’s data, enabling organizations to manage consent dynamically across systems. It is not limited to cookies (A), one-time logging (C), or initial requests (D).

“Consent tagging links an individual’s data with their recorded consent choices for compliant processing.”

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)

The best way to ensure an effective data privacy policy is implemented is to align regulatory requirements with business needs, because this will help achieve compliance while also supporting the organization’s objectives, values, and strategies. A data privacy policy should reflect the legal obligations and expectations of the organization, as well as the needs and preferences of its stakeholders, such as customers, employees, partners, and regulators. A data privacy policy should also be flexible and adaptable to changing circumstances and environments12.

The best answer is D. Assess the organization’s exposure related to the migration.

A comprehensive explanation is:

Before an organization migrates data from an on-premise solution to a cloud-hosted solution that spans more than one jurisdiction, it should first assess its exposure related to the migration. This means that the organization should identify and evaluate the potential risks and benefits of moving its data to the cloud, taking into account the legal, regulatory, contractual, and ethical obligations and implications of doing so.

Some of the factors that the organization should consider in its assessment are:

The nature, sensitivity, and value of the data being migrated, and the impact of its loss, theft, corruption, or disclosure on the organization and its stakeholders.

The security, privacy, and compliance requirements and standards that apply to the data in each jurisdiction where it is stored, processed, or accessed, and the differences or conflicts among them.

The trustworthiness, reliability, and reputation of the cloud service provider and its subcontractors, and the terms and conditions of their service level agreements (SLAs) and contracts.

The availability, performance, scalability, and cost-effectiveness of the cloud-hosted solution compared to the on-premise solution, and the trade-offs involved.

The technical feasibility and complexity of migrating the data from the on-premise solution to the cloud-hosted solution, and the tools and methods needed to do so.

The organizational readiness and capability to manage the change and transition from the on-premise solution to the cloud-hosted solution, and the training and support needed for the staff and users.

By conducting a thorough assessment of its exposure related to the migration, the organization can make an informed decision about whether to proceed with the migration or not, or under what conditions or modifications. The assessment can also help the organization to plan and implement appropriate measures and controls to mitigate or avoid any negative consequences and enhance or maximize any positive outcomes of the migration.

Ensuring data loss prevention (DLP) alerts are turned on (A), encrypting the data while it is being migrated (B), and conducting a penetration test of the hosted solution © are all good practices to protect data privacy and security when migrating data from an on-premise solution to a cloud-hosted solution that spans more than one jurisdiction. However they are not the first steps that should be done before the migration. They are more relevant during or after the migration process. They also do not address other aspects of exposure related to the migration, such as legal, regulatory, contractual, or ethical issues.

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.

A virtual private network (VPN) is a technology that creates a secure and encrypted connection over a public network, such as the internet. A VPN should be established first before authorizing remote access to a data store containing personal data, as it protects the data from unauthorized interception, modification, or disclosure by third parties. A VPN also helps to ensure the identity and authenticity of the remote users and devices accessing the data store. References: 2 Domain 2, Task 8

The primary consideration to protect against privacy violations when utilizing artificial intelligence (AI) driven business decisions is ensuring proper data sets are used to train the models. AI is a technology that enables machines or systems to perform tasks that normally require human intelligence, such as reasoning, learning, decision making, etc. AI relies on large amounts of data to train its models and algorithms to perform these tasks. However, if the data sets used to train the models are inaccurate, incomplete, biased, or outdated, they can result in privacy violations, such as discrimination, profiling, manipulation, or harm to the data subjects. Therefore, an IT privacy practitioner should ensure that the data sets used to train the models are proper, meaning that they are relevant, representative, reliable, and respectful of the data subjects’ rights and interests. References: : CDPSE Review Manual (Digital Version), page 141

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.

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

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

A privacy impact assessment (PIA) is a systematic process to identify and evaluate the potential privacy impacts of a system, project, program or initiative that involves the collection, use, disclosure or retention of personal data. A PIA should be done first to address privacy risk when migrating customer relationship management (CRM) data to a new system, as it would help to ensure that privacy risks are identified and mitigated before the migration is executed. A PIA would also help to ensure compliance with privacy principles, laws and regulations, and alignment with customer expectations and preferences. The other options are not as important as performing a PIA when addressing privacy risk when migrating CRM data to a new system. Developing a data migration plan is a process of defining and documenting the objectives, scope, approach, methods and steps for transferring data from one system to another, but it does not necessarily address privacy risk or impact. Conducting a legitimate interest analysis (LIA) is a process of assessing whether there is a legitimate interest for processing personal data that outweighs the rights and interests of the data subjects, but it is only applicable in certain jurisdictions and situations where legitimate interest is a valid legal basis for processing. Obtaining consent from data subjects is a process of obtaining their permission or agreement before collecting, using, disclosing or transferring their personal data for specific purposes, but it may not be required or sufficient for migrating CRM data to a new system, depending on the context and nature of the migration and the applicable laws and regulations1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

User consent to share personal data is the most important factor when designing APIs that enable mobile device applications to access personal data, as it ensures that the user is informed and agrees to the purpose, scope, and duration of the data sharing. User consent also helps to comply with the data protection principles and regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), that require user consent for certain types of data processing and sharing134. References: 1 Domain 2, Task 7

A primary objective of performing a privacy impact assessment (PIA) prior to onboarding a new Software as a Service (SaaS) provider for a customer relationship management (CRM) system is to identify controls to mitigate data privacy risks, such as data breaches, unauthorized access, misuse or loss of data. A PIA would help to evaluate the potential privacy impacts of using a new SaaS provider for CRM data processing activities, such as collecting, storing, analyzing or transferring customer data, and to implement appropriate controls to mitigate those impacts, such as encryption, access control, backup, audit trail or contractual clauses. A PIA would also help to ensure compliance with privacy principles, laws and regulations, and alignment with customer expectations and preferences. The other options are not primary objectives of performing a PIA prior to onboarding a new SaaS provider for CRM data processing activities. Classifying personal data according to the data classification scheme is an activity that may be part of a PIA process, but it is not an objective in itself. Assessing the risk associated with personal data usage is an activity that may be part of a PIA process, but it is not an objective in itself. Determining the service provider’s ability to maintain data protection controls is an activity that may be part of a PIA process, but it is not an objective in itself1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

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.

API keys are codes that are used to identify and authenticate an application or user when accessing an API. API keys could be stored insecurely, such as in plain text, in public repositories, or in unencrypted files. This could expose the API keys to unauthorized access, theft, or misuse by malicious actors, who could then access the API and the data it contains. This could result in data breaches, privacy violations, fraud, or other damages.

A privacy audit is a systematic and independent examination of an organization’s privacy policies, procedures, practices, and controls to assess their compliance with applicable laws, regulations, standards, and best practices. A privacy audit may result in various outputs, such as findings, recommendations, observations, or opinions. Among the options given, the output that is most likely to trigger remedial action is the identification of deficiencies in how personal data is shared with third parties. This is because such deficiencies may pose significant risks to the privacy and security of the data subjects, as well as to the reputation and legal liability of the organization. Remedial action may include implementing contractual safeguards, technical measures, or organizational changes to ensure that third parties respect and protect the personal data they receive from the organization.

Input validation controls are the best way to ensure consumer credit card numbers are accurately captured. Input validation controls are methods that check the format, type, range, and length of the input data before accepting, processing, or storing it. Input validation controls can help prevent errors, fraud, or data loss by rejecting invalid, incomplete, or malicious input. For example, input validation controls can verify that a credit card number follows the Luhn algorithm1, has the correct number of digits2, and matches the card issuer’s prefix3. Input validation controls can also prevent SQL injection attacks4 or cross-site scripting attacks5 that may compromise the security and privacy of the data.

Input reference controls, access controls, and reconciliation controls are also important for data quality and security, but they do not directly ensure the accuracy of consumer credit card numbers. Input reference controls are methods that compare the input data with a predefined list of values or a reference table to ensure consistency and validity. For example, input reference controls can check if a country name or a postal code is valid by looking up a database of valid values. Access controls are methods that restrict who can access, modify, or delete the data based on their roles, permissions, or credentials. For example, access controls can prevent unauthorized users from accessing or tampering with consumer credit card numbers. Reconciliation controls are methods that compare the data from different sources or systems to ensure completeness and accuracy. For example, reconciliation controls can check if the transactions recorded in the accounting system match the transactions processed by the payment gateway.

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)

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.

The first consideration for ensuring that endpoints are protected in line with the privacy policy is hardening the operating systems of endpoint devices. Hardening is a process of applying security configurations and controls to reduce the attack surface and vulnerabilities of an operating system. Hardening can include disabling unnecessary services and features, applying security patches and updates, enforcing strong passwords and encryption, configuring firewall and antivirus settings, and implementing least privilege principles. Hardening the operating systems of endpoint devices can help prevent unauthorized access, data leakage, malware infection, or other threats that may compromise the privacy of personal data stored or processed on those devices.

Detecting malicious access through endpoints, implementing network traffic filtering on endpoint devices, and managing remote access and control are also important aspects of endpoint security, but they are not the first consideration. Rather, they are dependent on or complementary to hardening the operating systems of endpoint devices. For example, detecting malicious access requires having a baseline of normal activity and behavior on the endpoint device, which can be established by hardening. Implementing network traffic filtering requires having a firewall or other network security tool installed and configured on the endpoint device, which is part of hardening. Managing remote access and control requires having authentication and authorization mechanisms in place on the endpoint device, which is also part of hardening.

Remote employee monitoring software is a solution that collects, analyzes and reports data on the activities and behaviors of employees who work remotely or from home. It can help organizations to measure and improve employee productivity, performance, engagement and security. However, it also poses significant privacy risks and challenges, as it may involve the collection and processing of personal data, such as names, email addresses, biometric data, IP addresses, keystrokes, screenshots, web browsing history, app usage, communication content and frequency, etc.

Data access should be restricted based on roles, meaning that only authorized and legitimate parties should be able to access and use the data collected by the remote employee monitoring software, based on their roles and responsibilities within the organization. This is a key privacy principle and practice that helps to protect the privacy rights and interests of the employees, and to prevent unauthorized or excessive access, use, disclosure or modification of their personal data by the organization or third parties. Data access restriction based on roles 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 implement appropriate technical and organizational measures to safeguard personal data.

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.

Pseudonymization is a technique that replaces direct identifiers in a data set with pseudonyms or artificial identifiers that do not reveal the identity of the data subjects. Pseudonymization is the best method to protect customers’ personal data that is forwarded to a central system for analysis, as it reduces the linkability of the data set with the original identity of the customers and thus enhances the privacy and security of the data. Pseudonymization also preserves some characteristics or patterns of the original data that can be used for analysis or research purposes, without compromising the accuracy or quality of the results. The other options are not as effective as pseudonymization in protecting customers’ personal data that is forwarded to a central system for analysis. Deletion is a technique that removes or destroys data from a storage device or media to prevent unauthorized access or recovery of the data, but it does not allow for any analysis or research purposes. Encryption is a technique that transforms plain text data into cipher text using an algorithm and a key, making it unreadable by unauthorized parties, but it does not reduce the linkability of the data set with the original identity of the customers and may require additional security measures to protect the encryption keys or certificates. Anonymization is a technique that removes or modifies all identifiers in a data set to prevent or limit the identification of the data subjects, but it may affect the accuracy or quality of the analysis or research results, as some characteristics or patterns of the original data may be lost or distorted1, p. 74-75 References: 1: CDPSE Review Manual (Digital Version)

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)

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

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

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.

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)

The best approach for a local office of a global organization faced with multiple privacy-related compliance requirements is to focus on the requirements with the highest organizational impact, because this will help prioritize the most critical and urgent privacy issues and risks that may affect the organization’s reputation, operations, or legal obligations. Focusing on the highest impact requirements will also help allocate the resources and efforts more efficiently and effectively, as well as align the local office’s privacy practices with the global organization’s objectives and strategies12.

The strategic goals of the organization should be established first before a privacy office starts to develop a data protection and privacy awareness campaign, because they provide the direction, purpose, and scope of the campaign. The strategic goals of the organization reflect its vision, mission, values, and objectives, as well as its alignment with the relevant privacy laws and regulations, stakeholder expectations, and industry best practices. The privacy office should design and implement the awareness campaign in a way that supports and promotes the strategic goals of the organization, as well as measures and evaluates its effectiveness and impact.

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

System hardening is a process of applying security measures and configurations to a system to reduce its attack surface and enhance its resistance to threats. System hardening can include disabling unnecessary services, removing default accounts, applying patches and updates, enforcing strong passwords and encryption, and implementing firewalls and antivirus software. The primary benefit of system hardening is that it increases system resiliency, which is the ability of a system to withstand or recover from adverse events that could affect its functionality or performance. The other options are not the primary benefits of system hardening, although they may be secondary benefits or outcomes. System hardening does not necessarily reduce external threats to data, as threats can originate from various sources and vectors. System hardening may reduce exposure of data, but only if the data is stored or processed by the system. System hardening does not eliminate attack motivation for data, as attackers may have different motives and incentives for targeting data. , p. 91-92 References: : CDPSE Review Manual (Digital Version)

A retention schedule is a document that specifies how long different types of records or data should be kept and when they should be deleted or disposed of, based on legal, regulatory, operational or historical requirements. A retention schedule is the best indication of an effective records management program for personal data, 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. A retention schedule 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 indicative of an effective records management program for personal data as a retention schedule. Archived data is used for future analytics may indicate that the organization is leveraging its data assets for business intelligence or research purposes, but it may not comply with the principles of data minimization and storage limitation, or the privacy rights and preferences of the data subjects. The legal department has approved the retention policy may indicate that the organization has obtained legal advice or guidance on its records management program for personal data, but it may not reflect the actual implementation or execution of the retention policy. All sensitive data has been tagged may indicate that the organization has implemented a data classification scheme for its records or data, but it may not indicate how long the records or data should be kept or when they should be deleted or disposed of1, p. 99-100 References: 1: CDPSE Review Manual (Digital Version)

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

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

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

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]

The best way for an organization to maintain the effectiveness of its privacy breach incident response plan is to conduct annual data privacy tabletop exercises. A data privacy tabletop exercise is a simulated scenario that tests the organization’s ability to respond to a privacy breach incident, such as a data breach, leak, or misuse. A data privacy tabletop exercise involves key stakeholders, such as the privacy office, the information security team, the legal counsel, the public relations team, etc., who role-play their actions and decisions based on the scenario. A data privacy tabletop exercise helps to evaluate and improve the organization’s privacy breach incident response plan, such as identifying gaps or weaknesses, validating roles and responsibilities, verifying procedures and protocols, assessing communication and coordination, etc. References: : CDPSE Review Manual (Digital Version), page 83

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.