CDPSE Exam Dumps - Certified Data Privacy Solutions Engineer

A thin client remote desktop protocol (RDP) is the most effective remote access model for reducing the likelihood of attacks originating from connecting devices, because it minimizes the amount of data and processing that occurs on the remote device. A thin client RDP only sends keyboard, mouse and display information between the remote device and the server, while the actual processing and storage of data happens on the server. This reduces the exposure of sensitive data and applications to potential attackers who may compromise the remote device.

References:

• CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.3 – Privacy Architecture Implementation1.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 – Privacy Architecture, Section 2.4 – Remote Access2.

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.

References:

• Mobile Workforce Security Considerations and Privacy - ISACA, section 3: “The principle of least privilege should be applied to ensure that only authorized personnel have access to the data.”
• Why Employee Privacy Matters More Than Ever - ISACA, section 3: “Privacy-first monitoring should include granular privacy controls, including: Auto-redacting personal information; Restricting access to sensitive information based on role; Masking sensitive information from view.”

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 an IT privacy practitioner’s best recommendation to reduce privacy risk before an organization provides personal data to a third party, as it would protect the privacy of the data subjects 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 research purposes by the third party, without compromising the accuracy or quality of the results. The other options are not as effective as anonymization in reducing privacy risk before an organization provides personal data to a third party. Tokenization is a technique that replaces sensitive or confidential data with non-sensitive tokens or placeholders that do not reveal the original data, but it does not prevent or limit the identification of the data subjects, as tokens can be reversed or linked back to the original data using a tokenization system or key. Aggregation is a technique that combines individual data into groups or categories that do not reveal the identity of the data subjects, but it may not prevent or limit the identification of the data subjects, as aggregated data can be de-aggregated or re-identified using other sources of information or techniques. 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 prevent or limit the identification of the data subjects, as encrypted data can be decrypted or linked back to the original data using an encryption system or key1, p. 74-75 References: 1: CDPSE Review Manual (Digital Version)

Reviewing proposed privacy rules that govern the processing of personal data is the most useful action to help define the scope of the project because it helps identify the legal and regulatory requirements, the data protection principles and the privacy objectives that the information security controls need to support. Reviewing recent audit reports, identifying databases that contain personal data or do not have encryption in place are helpful actions to assess the current state of privacy and security, but they do not provide a clear direction for the project scope.

References:

• CDPSE Review Manual (Digital Version), Domain 2: Privacy Architecture, Task 2.1: Identify and/or define privacy requirements1
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3: Privacy Architecture, Section: Privacy Requirements2

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

Some organizations, typically those that manage large amounts of personal information related to employees, customers, or constituents, will employ a chief privacy officer (CPO). Some organizations have a CPO because applicable regulations such as the Gramm-Leach-Bliley Act (GLBA) require it. Other regulations such as the Health Information Portability and Accountability Act (HIPAA), the Fair Credit Reporting Act (FCRA), and the GLBA place a slate of responsibilities upon an organization that compels them to hire an executive responsible for overseeing compliance.

The chief privacy officer (CPO) is the senior executive who is responsible for establishing and maintaining the organization’s privacy vision, strategy, and program. The CPO oversees the development and implementation of privacy policies, procedures, standards, and controls, and ensures that they align with the organization’s business objectives and legal obligations. The CPO also leads the privacy governance structure, such as the privacy steering committee, and coordinates with other stakeholders, such as the chief data officer (CDO), the information security steering committee, and the legal counsel, to ensure that privacy is integrated into all aspects of the organization’s operations. References: : CDPSE Review Manual (Digital Version), page 21

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.

References:

• ISACA, CDPSE Review Manual 2021, Chapter 2: Privacy Governance, Section 2.1: Privacy Governance Framework, p. 35-36.
• ISACA, Data Privacy Audit/Assurance Program, Control Objective 1: Data Privacy Governance, p. 4-51

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.

References:

• CDPSE Exam Content Outline, Domain 1 – Privacy Governance (Governance, Management & Risk Management), Task 3: Participate in the evaluation of privacy policies, programs and policies for their alignment with legal requirements, regulatory requirements and/or industry best practices3.
• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.2 – Privacy Policy4.

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.

References: Luhn algorithm, Credit card number, Bank card number, SQL injection, Cross-site scripting

The role primarily assigned to an internal data owner is authorizing access rights. A data owner is a person or a role within the organization who has the authority and responsibility for the data assets under their control. A data owner is responsible for defining the data classification, data quality, data retention, and data security requirements for their data assets. A data owner is also responsible for granting, revoking, and reviewing the access rights to their data assets based on the principle of least privilege and the business needs. A data owner is accountable for ensuring that the data assets are used in compliance with the organizational policies and the applicable laws and regulations. References:

• [ISACA Glossary of Terms]
• [ISACA CDPSE Review Manual, Chapter 3, Section 3.2.1]
• [ISACA CDPSE Review Manual, Chapter 3, Section 3.2.2]
• [ISACA CDPSE Review Manual, Chapter 3, Section 3.2.3]

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 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]

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

Awareness campaigns are initiatives that aim to educate and inform employees about the importance of privacy protection, the organization’s privacy policies and procedures, the applicable laws and regulations, and the best practices and behaviors to safeguard personal data. Awareness campaigns can support an organization’s efforts to create and maintain desired privacy protection practices among employees by raising their awareness, understanding and commitment to privacy, as well as by influencing their attitudes, values and culture. Awareness campaigns can use various methods and channels, such as posters, newsletters, videos, webinars, quizzes, games or events, to deliver consistent and engaging messages to the target audience. The other options are not the best ways to support an organization’s efforts to create and maintain desired privacy protection practices among employees. Skills training programs are focused on developing specific technical or functional skills related to privacy, but they may not address the broader aspects of privacy awareness or culture. Performance evaluations are focused on measuring and rewarding individual or team performance based on predefined criteria or objectives, but they may not reflect the actual level of privacy awareness or practice. Code of conduct principles are focused on establishing and enforcing ethical standards and rules of behavior for employees, but they may not be sufficient to create or maintain privacy awareness or practice without effective communication and education1, p. 103-104 References: 1: CDPSE Review Manual (Digital Version)

To ensure the protection of personal data, privacy policies should mandate that access to information system applications be authorized by the business application owner, because they are the ones who are responsible for defining the business requirements, functions, and objectives of the applications. The business application owner can also determine the appropriate level of access for different users or groups based on their roles, responsibilities, and needs. The business application owner can also monitor and review the access control policies and procedures to ensure that they are effective and compliant with the privacy regulations and standards.

References:

• Access Control Policy and Implementation Guides, CSRC
• What is Authorization and Access Control?, ICANN

An audit log is a record of the activities and events that occur in an information system, such as an application hosting personal data. An audit log can help to monitor, detect, investigate and prevent unauthorized or malicious access, use, modification or deletion of personal data. An audit log can also help to demonstrate compliance with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). An audit log should capture the following information for each event: 9

• The date and time of the event
• The identity of the user or system that performed the event
• The type and description of the event
• The outcome or result of the event
• The personal data that were accessed, used, modified or deleted

The last user who accessed personal data is the most important information to capture in the audit log, as it can help to identify who is responsible for any data breach or misuse of personal data. It can also help to verify that only authorized and legitimate users have access to personal data, and that they follow the data use policy and the principle of least privilege. The last user who accessed personal data can also help to support data subjects’ rights, such as the right to access, rectify, erase or restrict their personal data.

The other options are less important or irrelevant to capture in the audit log of an application hosting personal data. Server details of the hosting environment are not related to personal data, and they can be obtained from other sources, such as network logs or configuration files. Last logins of privileged users are important to capture in a separate audit log for user account management, but they do not indicate what personal data were accessed or used by those users. Application error events are important to capture in a separate audit log for system performance and reliability, but they do not indicate what personal data were affected by those errors.

References:

• IS Audit Basics: Auditing Data Privacy, section 4: “Audit logs should be maintained for all systems that process PII.”
• Data Protection Audit Manual, section 3.2: “Audit trails should be kept for all processing operations involving personal data.”
• Audit Logging Best Practices, section 2: “An audit log entry should contain enough information to answer who did what and when.”

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

One of the foundational goals of data privacy laws is to give people rights over the collection of personal data, such as the right to access, correct, delete, or object to the processing of their data. Privacy laws also aim to protect people’s dignity, autonomy, and self-determination in relation to their personal data. The other options are not accurate or complete descriptions of the purpose of data privacy laws.

References:

• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.1 – Privacy Principles1.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 1 – Privacy Governance, Section 1.2 – Data Privacy Laws and Regulations2.

The primary consideration of a multinational organization deploying a user and entity behavior analytics (UEBA) tool to centralize the monitoring of anomalous employee behavior is cross-border data transfer, because it may involve the transfer of personal data across different jurisdictions with different privacy laws and regulations. The organization needs to ensure that it complies with the applicable legal requirements and safeguards the privacy rights of its employees when transferring their data to a central location for analysis. The other options are secondary or operational considerations that may not have a significant impact on the privacy of the employees.

References:

• CDPSE Exam Content Outline, Domain 2 – Privacy Architecture (Privacy Architecture Implementation), Task 3: Implement privacy solutions1.
• CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.4 – Cross-Border Data Transfer2.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 – Privacy Architecture, Section 2.5 – Cross-Border Data Transfer3.

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]

Code review is a primary element of application and software hardening. Code review is a process of examining the source code of an application or software to identify and fix errors, vulnerabilities, or inefficiencies that may compromise its functionality, security, or performance. Code review can help prevent common security risks such as buffer overflows, SQL injections, cross-site scripting, or logic flaws. Code review can also help improve the quality, readability, maintainability, and usability of the code. Code review can be done manually by developers or peers, or automatically by tools such as static code analyzers or code quality checkers.

Vulnerability analysis, database configuration, and software repository are also important for application and software hardening, but they are not primary elements. Vulnerability analysis is a process of identifying and assessing the weaknesses or flaws in an application or software that may expose it to attacks or exploitation. Vulnerability analysis can be done by tools such as vulnerability scanners or penetration testers. Database configuration is a process of setting up and managing the parameters, options, or features of a database system that stores or processes data for an application or software. Database configuration can include aspects such as access control, encryption, backup, recovery, performance tuning, or replication. Software repository is a location where the source code, binaries, or documentation of an application or software are stored and managed. Software repository can facilitate version control, collaboration, distribution, or deployment of the application or software.

References: What is Application Hardening ? - GeeksforGeeks, What is OS Hardening and How Can Developers Implement it, System Hardening: An Easy-to-Understand Overview - Trenton Systems

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.

References:

• PIA Triggers - International Association of Privacy Professionals
• Privacy Impact Assessment - International Association of Privacy Professionals
• GDPR Privacy Impact Assessment
• Data Protection Impact Assessment triggers: Clarity or confusion?

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)

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 tabletop exercise is a simulated scenario that tests the organization’s ability to respond to a privacy breach incident in a realistic and interactive way. A tabletop exercise can help the organization to evaluate the roles and responsibilities of the incident response team, identify the gaps and weaknesses in the plan, improve the communication and coordination among the stakeholders, and update the plan based on the lessons learned and best practices12. A tabletop exercise can also enhance the awareness and readiness of the organization to handle privacy breach incidents in a timely and effective manner3. References:

• ISACA CDPSE Review Manual, Chapter 4, Section 4.3.2
• ISACA Journal, Volume 4, 2019, “Tabletop Exercises: Three Sample Scenarios”
• ISACA Journal, Volume 6, 2017, “Privacy Breach Response: Preparing for the Inevitable”

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

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 when planning a new implementation for tracking consumer web browser activity, as it would help to ensure that privacy risks are identified and mitigated before the implementation is executed. A PIA would also help to ensure compliance with privacy principles, laws and regulations, and alignment with consumer expectations and preferences. The other options are not as important as conducting a PIA when planning a new implementation for tracking consumer web browser activity. Seeking approval from regulatory authorities may be required or advisable for some types of personal data or data processing activities, but it may not be necessary or sufficient for tracking consumer web browser activity, depending on the context and jurisdiction. Obtaining consent from the organization’s clients may be required or advisable for some types of personal data or data processing activities, but it may not be necessary or sufficient for tracking consumer web browser activity, depending on the context and jurisdiction. Reviewing and updating the cookie policy may be required or advisable for some types of personal data or data processing activities, but it may not be necessary or sufficient for tracking consumer web browser activity, depending on the context and jurisdiction1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

The most useful information for prioritizing database selection for encryption is the asset classification scheme. An asset classification scheme is a system of organizing and categorizing assets based on their value, sensitivity, criticality, or risk level. An asset classification scheme helps to determine the appropriate level of protection or handling for each asset. For example, an asset classification scheme may assign labels such as public, internal, confidential, or secret to different types of data based on their impact if compromised. Databases that contain higher-classified data should be prioritized for encryption to prevent unauthorized access, disclosure, or modification.

Database administration audit logs, historical security incidents, or penetration test results are also useful information for database security, but they are not the most useful for prioritizing database selection for encryption. Database administration audit logs are records of activities performed by database administrators or other privileged users on the database system. Database administration audit logs help to monitor and verify the actions and changes made by authorized users and detect any anomalies or violations. Historical security incidents are records of events that have compromised or threatened the security of the database system in the past. Historical security incidents help to identify and analyze the root causes, impacts, and lessons learned from previous breaches or attacks. Penetration test results are reports of simulated attacks performed by ethical hackers or security experts on the database system to evaluate its vulnerabilities and defenses. Penetration test results help to discover and exploit any weaknesses or gaps in the database security posture and recommend remediation actions.

References: Data Classification Policy - SANS Institute, Database Security Best Practices - Oracle, [Database Security: An Essential Guide | IBM]

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

Before de-identifying data, it is important to determine the categories of personal data collected, such as names, addresses, phone numbers, email addresses, social security numbers, health information, and so on. This will help to identify which data elements are considered identifiers or quasi-identifiers, and which de-identification techniques are appropriate for each category. For example, some data elements may need to be removed completely, while others may be masked, generalized, or perturbed.

References:

• Anonymize and De-identify | Research Data Management
• Data De-identification: An Overview of Basic Terms - ed

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.

References:

• Data Migration: On-Premise to Cloud - 10 Steps to Success1
• 8 Best Practices for On-Premises to Cloud Migration2
• 5 Steps for a Successful On-Premise to Cloud Migration3
• Extend on-premises data solutions to the cloud4
• On Premise to Cloud migration tool5

Collecting only the data necessary to meet objectives is the best approach to minimize privacy risk when collecting personal data. This is based on the principle of data minimization, which states that personal data should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. Using a third party, collecting data through a secure web server, or aggregating data immediately may reduce some privacy risks, but they do not eliminate the possibility of collecting excessive or unnecessary data. References: CDPSE Exam Content Outline, Domain 3, Task 3.2

Data retention is a process of keeping personal data for a specified period of time for legitimate purposes, such as legal obligations, contractual agreements, business operations or historical records. Data retention should be based on the principle of data minimization, which requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. Data retention should also comply with the principle of storage limitation, which requires deleting or disposing of personal data when it is no longer needed or justified. The most likely valid use case for keeping a customer’s personal data after contract termination is a required retention period due to regulations, such as tax laws, financial laws, health laws or consumer protection laws, that mandate the organization to retain certain types of customer data for a certain period of time after the end of the contractual relationship. The other options are not valid use cases for keeping a customer’s personal data after contract termination, as they do not meet the criteria of necessity, relevance or justification. For the purpose of medical research, the organization would need to obtain the consent of the customer or have another legal basis for processing their personal data for a different purpose than the original contract. A forthcoming campaign to win back customers or ease of onboarding when the customer returns are not legitimate purposes for retaining customer data after contract termination, as they are not related to the original contract and may violate the customer’s privacy rights and preferences. , p. 99-100 References: : CDPSE Review Manual (Digital Version)

However, the risks associated with long-term retention have compelled organizations to consider alternatives; one is data archival, the process of preparing data for long-term storage. When organizations are bound by specific laws to retain data for many years, archival provides a viable opportunity to remove data from online transaction systems to other systems or media.

Data archiving is the process of moving data that is no longer actively used to a separate storage device for long-term retention. Data archiving helps to reduce the cost and complexity of data storage, improve the performance and availability of data systems, and comply with data retention policies and regulations. Data archiving should document controls relating to periods of retention for personal data, such as the criteria for determining the retention period, the procedures for deleting or anonymizing data after the retention period expires, and the mechanisms for ensuring the integrity and security of archived data. References: : CDPSE Review Manual (Digital Version), page 123

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)

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

Before using data from the organization’s customer database for a new marketing application, the first step should be to determine what data is required by the application and for what purpose. This will help to ensure that the data collection and processing are relevant, necessary, and proportionate to the intended use, and that the data minimization principle is followed. Data minimization means that only the minimum amount of personal data needed to achieve a specific purpose should be collected and processed, and that any excess or irrelevant data should be deleted or anonymized1. This will also help to comply with the data privacy laws and regulations that apply to the organization, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which require organizations to inform data subjects about the types and purposes of data processing, and to obtain their consent if needed23.

References:

• ISACA, Data Privacy Audit/Assurance Program, Control Objective 2: Data Minimization, p. 61
• ISACA, GDPR Data Protection Impact Assessments, p. 4-52
• ISACA, CCPA vs. GDPR: Similarities and Differences, p. 1-23

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.

References: Manage endpoint security policies in Microsoft Intune, ENDPOINT SECURITY POLICY, How To Build An Effective Endpoint Security Policy And Prevent Cyberattacks

Restricting access to authorized users is the best control to secure application programming interfaces (APIs) that may contain personal information, as it would prevent unauthorized access, modification or disclosure of the personal information by third parties or intermediaries. Restricting access to authorized users can be achieved by using various methods, such as authentication, authorization, encryption, tokens or certificates. The other options are not effective controls to secure APIs that may contain personal information. Encrypting APIs with the organization’s private key is not a feasible or desirable method, as it would make the APIs unreadable by anyone who does not have the corresponding public key, which would defeat the purpose of using APIs for interoperability and integration. Requiring nondisclosure agreements (NDAs) when sharing APIs is not a reliable or enforceable method, as it would depend on the compliance and cooperation of the parties who receive the APIs, and it would not prevent unauthorized access, modification or disclosure of the personal information by third parties or intermediaries who are not bound by the NDAs. Sharing only digitally signed APIs is not a sufficient method, as it would only ensure the authenticity and integrity of the APIs, but it would not prevent unauthorized access, modification or disclosure of the personal information by third parties or intermediaries who can read or intercept the APIs1, p. 90-91 References: 1: CDPSE Review Manual (Digital Version)

Validating the privacy framework is a responsibility of the audit function in helping an organization address privacy compliance requirements, as it would help to verify and validate the effectiveness and adequacy of the privacy framework implemented by the organization to comply with privacy principles, laws and regulations. Validating the privacy framework would also help to identify and report any gaps, weaknesses or issues in the privacy framework, and to provide recommendations for improvement or remediation. The other options are not responsibilities of the audit function in helping an organization address privacy compliance requirements. Approving privacy impact assessments (PIAs) is a responsibility of management or governance function in helping an organization address privacy compliance requirements, as they would have authority and accountability for approving PIAs conducted by project teams or business units before implementing any system, project, program or initiative that involves personal data processing activities. Managing privacy notices provided to customers is a responsibility of operational function in helping an organization address privacy compliance requirements, as they would have direct contact and interaction with customers and would be responsible for providing clear and accurate information about how their personal data is collected, used, disclosed and transferred by the organization. 

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)

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.

References:

• ISACA, CDPSE Review Manual 2021, Chapter 3: Privacy by Design, Section 3.2: Privacy Engineering, p. 97-98.