CDPSE Certified Data Privacy Solutions Engineer Questions and Answers

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

A 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.

Online behavioral tracking is a tracking technology associated with unsolicited targeted advertisements that presents the greatest privacy risk. Online behavioral tracking is a technique that collects and analyzes personal data about users’ online activities, preferences, interests, and behaviors across different websites or platforms. Online behavioral tracking is used to create user profiles and deliver personalized or targeted advertisements that match users’ needs or wants. Online behavioral tracking poses a privacy risk because it can invade users’ privacy by collecting sensitive or intimate personal data without their knowledge or consent, such as health conditions, political views, sexual orientation, etc. Online behavioral tracking can also expose users to unwanted or inappropriate advertisements that may influence their decisions or actions. References: : CDPSE Review Manual (Digital Version), page 139

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

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

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)

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

References:

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

A peer-to-peer (P2P) system architecture is a network model where each node (peer) can act as both a client and a server, and communicate directly with other peers without relying on a centralized authority or intermediary. A P2P system architecture best supports anonymity for data transmission, by providing the following advantages:

• It can hide the identity and location of the peers, by using encryption, pseudonyms, proxies, or onion routing techniques, such as Tor1 or I2P2. These techniques can prevent eavesdropping, tracking, or censorship by third parties, such as Internet service providers, governments, or hackers.
• It can distribute the data across multiple peers, by using hashing, replication, or fragmentation techniques, such as BitTorrent3 or IPFS4. These techniques can reduce the risk of data loss, corruption, or tampering by malicious peers, and increase the availability and resilience of the data.
• It can enable the peers to control their own data, by using consensus, validation, or incentive mechanisms, such as blockchain5 or smart contracts. These mechanisms can ensure the integrity and authenticity of the data transactions, and enforce the privacy policies and preferences of the data owners.

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 best practice to protect data privacy when disposing removable backup media is B. Data sanitization.

A comprehensive explanation is:

Data sanitization is the process of permanently and irreversibly erasing or destroying the data on a storage device or media, such as a hard drive, a USB drive, a CD/DVD, etc. Data sanitization ensures that the data cannot be recovered or reconstructed by any means, even by using specialized software or hardware tools. Data sanitization is also known as data wiping, data erasure, data destruction, or data disposal.

Data sanitization is the best practice to protect data privacy when disposing removable backup media because it prevents unauthorized access, disclosure, theft, or misuse of the sensitive or confidential data that may be stored on the media. Data sanitization also helps to comply with the legal and regulatory requirements and standards for data protection and privacy, such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI DSS), etc.

There are different methods and techniques for data sanitization, depending on the type and format of the storage device or media. Some of the common methods are:

• Overwriting: Overwriting replaces the existing data on the device or media with random or meaningless data, such as zeros, ones, or patterns. Overwriting can be done multiple times to increase the level of security and assurance. Overwriting is suitable for magnetic media, such as hard disk drives (HDDs) or tapes.
• Degaussing: Degaussing exposes the device or media to a strong magnetic field that disrupts and destroys the magnetic structure and alignment of the data. Degaussing renders the device or media unusable and unreadable. Degaussing is suitable for magnetic media, such as hard disk drives (HDDs) or tapes.
• Physical Destruction: Physical destruction involves applying physical force or damage to the device or media that breaks it into small pieces or shreds it. Physical destruction can be done by using mechanical tools, such as shredders, crushers, drills, hammers, etc., or by using thermal methods, such as incineration, melting, etc. Physical destruction is suitable for any type of media, such as hard disk drives (HDDs), solid state drives (SSDs), USB drives, CDs/DVDs, etc.

Data encryption (A) is not a good practice to protect data privacy when disposing removable backup media because it does not erase or destroy the data on the media. Data encryption only transforms the data into an unreadable format that can only be accessed with a key or a password. However, if the key or password is lost, stolen, compromised, or guessed by an attacker, the data can still be decrypted and exposed. Data encryption is more suitable for protecting data in transit or at rest, but not for disposing data.

Data scrambling © is not a good practice to protect data privacy when disposing removable backup media because it does not erase or destroy the data on the media. Data scrambling only rearranges the order of the bits or bytes of the data to make it appear random or meaningless. However, if the algorithm or pattern of scrambling is known or discovered by an attacker, the data can still be unscrambled and restored. Data scrambling is more suitable for obfuscating data for testing or debugging purposes, but not for disposing data.

Data masking (D) is not a good practice to protect data privacy when disposing removable backup media because it does not erase or destroy the data on the media. Data masking only replaces some parts of the data with fictitious or anonymized values to hide its true identity or meaning. However, if the original data is still stored somewhere else or if the masking technique is weak or reversible by an attacker, the data can still be unmasked and revealed. Data masking is more suitable for protecting data in use or in analysis, but not for disposing data.

References:

• What Is Data Sanitization?1
• How to securely erase hard drives (HDDs) and solid state drives (SSDs)2
• Secure Data Disposal & Destruction: 6 Methods to Follow3

The best approach for handling personal data that has been restricted is to flag users’ email addresses to make sure they do not receive promotional information, because this will respect the users’ preferences and rights to opt out of marketing communications. This will also help the company comply with the data protection laws and regulations that require consent and transparency for sending marketing emails, such as the General Data Protection Regulation (GDPR) and the CAN-SPAM Act12. The other options are not appropriate or sufficient for handling restricted data, because they may violate the users’ rights, expectations, or agreements, or cause operational issues for the company.

References:

• CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.1 – Data Classification3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3 – Data Lifecycle, Section 3.2 – Data Classification4.

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]

D. Requiring employees to review the organization’s privacy policy on an annual basis

Short Explanation: Requiring employees to review the organization’s privacy policy on an annual basis is the best activity to enable an organization to identify gaps in its privacy posture because it can help to ensure that the employees are aware of the current privacy requirements, expectations, and practices of the organization. It can also help to identify any discrepancies, inconsistencies, or conflicts between the policy and the actual implementation of privacy controls and processes. By reviewing the policy regularly, the organization can also update and improve it as needed to reflect any changes in the privacy landscape, such as new laws, regulations, standards, or threats.

References:

• Privacy Policy Review Checklist, PrivacySense
• How to Write a Privacy Policy for Your Website, TermsFeed

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

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

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)

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

Anonymization is a technique that removes or modifies personal data in such a way that it can no longer be attributed to a specific data subject. Anonymization can be achieved by various methods, such as encryption, pseudonymization, aggregation, generalization, etc. When using anonymization techniques to prevent unauthorized access to personal data, the most important consideration to ensure the data is adequately protected is that the key must be kept separate and distinct from the data it protects. The key is a piece of information that is used to reverse the anonymization process and restore the original personal data. The key must be stored and managed in a secure location that is different from where the anonymized data is stored and processed. This way, even if the anonymized data is compromised, the key cannot be accessed or used to re-identify the data subjects. References: : CDPSE Review Manual (Digital Version), page 29

Domain name system (DNS) sinkhole is a technology that redirects malicious or unwanted domain names to alternative destinations, such as a fake or harmless website, a warning page, or a null address. DNS sinkhole is the most effective technology deployed at an enterprise level to block malicious tracking of user internet browsing, as it would prevent users from accessing websites that use tracking technologies, such as cookies, web beacons, or fingerprinting, to collect and analyze user behavior or preferences. DNS sinkhole would also protect users from other malicious activities, such as malware distribution, phishing attempts, or botnet command and control. The other options are not as effective as DNS sinkhole in blocking malicious tracking of user internet browsing at an enterprise level. Web application firewall (WAF) is a technology that monitors and filters incoming and outgoing web traffic to protect web applications from attacks, such as cross-site scripting (XSS), SQL injection, or denial-of-service (DoS), but it does not block malicious tracking of user internet browsing. Website URL blacklisting is a method of blocking access to websites that are known or suspected to be malicious or harmful, but it does not block malicious tracking of user internet browsing from unknown or legitimate websites that use tracking technologies. Desktop antivirus software is a technology that scans and removes viruses, malware, spyware, or other threats from desktop computers or devices, but it does not block malicious tracking of user internet browsing from websites that use tracking technologies1, p. 92 References: 1: CDPSE Review Manual (Digital Version)

The best way to maintain the quality of personal data is to implement routine automatic validation, which is a process of checking the accuracy, completeness, consistency, and timeliness of the data using automated tools or scripts. Routine automatic validation can help identify and correct any errors, anomalies, or discrepancies in the data, as well as ensure that the data meets the specified quality standards and requirements. Routine automatic validation can also help improve the efficiency and reliability of the data processing and analysis12.

References:

• CDPSE Exam Content Outline, Domain 3 – Data Lifecycle (Data Quality), Task 2: Implement data quality measures3.
• CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.2 – Data Quality4.

A 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 conducted as early as possible in the system lifecycle, preferably during the development stage, to ensure that privacy risks are identified and mitigated before the system is deployed. Conducting a PIA during functional testing, UAT or production stages may be too late to address privacy issues effectively and may result in costly rework or delays1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

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

References:

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

 Transparency is the most important attribute of a privacy policy because it informs the users about how their personal data is collected, used, shared, and protected by the organization. Transparency also helps to build trust and confidence with the users, and to comply with legal and ethical obligations regarding data privacy.

References:

• ISACA Certified Data Privacy Solutions Engineer Study Guide, Domain 2: Privacy Governance, Task 2.1: Develop and implement privacy policies and procedures, p. 49-50.
• What is a Privacy Policy? | Privacy Policies

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]

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)

Following an established privacy framework is the most important step when developing an organizational data privacy program because it provides a structured and consistent approach to identify, assess, and manage privacy risks and compliance obligations. A privacy framework can also help to align the privacy program with the organization’s strategic goals, values, and culture, as well as to communicate and demonstrate the privacy program’s effectiveness to internal and external stakeholders. Some examples of established privacy frameworks are the NIST Privacy Framework, the ISO/IEC 27701:2019, and the AICPA Privacy Maturity Model.

References:

• NIST Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management, NIST
• ISO/IEC 27701:2019 Security techniques — Extension to ISO/IEC 27001 and ISO/IEC 27002 for privacy information management — Requirements and guidelines, ISO
• Privacy Maturity Model, AICPA

A third-party privacy control assessment is an independent and objective evaluation of the design and effectiveness of the privacy controls implemented by an organization to protect personal data and comply with privacy laws and regulations. A third-party privacy control assessment can help senior management to verify the success of its commitment to privacy by design, by providing the following benefits:

• It can measure the extent to which the organization has adopted and integrated the principles and practices of privacy by design throughout its products, services, processes and systems.
• It can identify the strengths and weaknesses of the organization’s privacy governance, policies, procedures, standards and guidelines, and provide recommendations for improvement.
• It can validate the organization’s compliance with the applicable privacy requirements and expectations of its customers, stakeholders, regulators and auditors.
• It can enhance the organization’s reputation and trustworthiness as a responsible and transparent data controller and processor.

The other options are less effective or irrelevant for verifying the success of the commitment to privacy by design. Reviewing the findings of an industry benchmarking assessment may provide some insights into how the organization compares with its peers or competitors in terms of privacy performance, but it may not reflect the specific privacy goals, risks and challenges of the organization. Identifying trends in the organization’s amount of compromised personal data or number of privacy incidents may indicate some aspects of the organization’s privacy maturity, but they are reactive and lagging indicators that do not capture the proactive and preventive nature of privacy by design. Moreover, these metrics may not account for other factors that may influence the occurrence or impact of data breaches or privacy violations, such as external threats, human errors or environmental changes.

References:

• Privacy by Design: How Far Have We Come? - ISACA, section 1: “Privacy by design challenges conventional system thinking. It mandates that any system, process or infrastructure that uses personal data consider privacy throughout its development life cycle.”
• Privacy Control Assessment - ISACA, section 1: “A Privacy Control Assessment (PCA) is an independent evaluation performed by a qualified assessor to determine whether an entity’s controls are suitably designed and operating effectively to meet its objectives related to protecting personal information.”
• Privacy by Design: The New Competitive Advantage - ISACA, section 2: “Privacy by design is a proactive approach to embedding privacy into the design specifications of various technologies, business practices and networked infrastructure.”

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

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

The value proposition of a PIA is not understood by management is the greatest obstacle to conducting a PIA, as it may result in lack of support, funding, resources or commitment for the PIA process and outcomes. Management may not appreciate or recognize the benefits of a PIA, such as enhancing privacy protection, reducing privacy risks and costs, increasing customer trust and satisfaction, and complying with privacy laws and regulations. Management may also perceive a PIA as a burden, a delay or a hindrance to the system or project development and delivery. The other options are not as significant as the value proposition of a PIA is not understood by management as obstacles to conducting a PIA. Conducting a PIA requires significant funding and resources is an obstacle to conducting a PIA, but it may be overcome by demonstrating the return on investment or the cost-benefit analysis of a PIA. PIAs need to be performed many times in a year is an obstacle to conducting a PIA, but it may be mitigated by adopting a scalable or modular approach to PIAs that can be tailored to different types or levels of systems or projects. The organization lacks knowledge of PIA methodology is an obstacle to conducting a PIA, but it may be resolved by acquiring or developing the necessary skills, tools or guidance for performing PIAs1, p. 67-68 References: 1: CDPSE Review Manual (Digital Version)

A 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)

Inferred data is the type of data that is produced by using a more complex method of analytics to find correlations between data sets and using them to categorize or profile people. Inferred data is not directly observed or collected from the data subjects, but rather derived from other sources of data, such as behavioral, transactional, or demographic data. Inferred data can be used to make assumptions or predictions about the data subjects’ preferences, interests, behaviors, or characteristics12.

References:

• CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.1 – Data Classification3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3 – Data Lifecycle, Section 3.2 – Data Classification4.

Encryption is a security practice that transforms data into an unreadable format using a secret key or algorithm. Encryption protects the confidentiality and integrity of data, especially when they are transferred using email or other communication channels. Encryption ensures that only authorized parties can access and use the data, while unauthorized parties cannot decipher or modify the data without the key or algorithm. Encryption 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.

Centrally managed encryption is a type of encryption that is implemented and controlled by a central authority or system, such as an organization or a service provider. Centrally managed encryption has the following advantages over end user-managed encryption, private cloud storage space, or password-protected .zip files, for reducing the risk of compromise when transferring personal information using email:

• It can enforce consistent and standardized encryption policies and procedures across the organization or the service, such as the encryption standards, algorithms, keys, modes, and formats.
• It can automate the encryption and decryption processes for the users, without requiring them to perform any manual actions or install any software or plug-ins on their devices.
• It can monitor and audit the encryption activities and incidents, and provide visibility and accountability for the data protection and compliance status.
• It can reduce the human errors or negligence that may compromise the encryption security, such as losing or sharing the keys, forgetting or reusing the passwords, or sending the data to the wrong recipients.

References:

• Encryption in the Hands of End Users - ISACA, section 2: “A key goal of encryption is to protect the file even when direct access is possible or the transfer is intercepted.”
• The Complexity Conundrum: Simplifying Data Security - ISACA, section 3: “Centrally managed encryption solutions can help enterprises overcome these challenges by providing a unified platform for encrypting data across different environments and applications.”
• Email Encryption: What You Need to Know - Lifewire, section 1: “Email encryption is a way of protecting your email messages from being read by anyone other than the intended recipients.”

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”

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

References:

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

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

A comprehensive explanation is:

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

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

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

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

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

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

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

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

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

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

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

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

References:

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

The best way to validate that privacy practices align to the published enterprise privacy management program is to conduct an audit. An audit is an independent and objective examination of evidence to provide assurance that privacy practices are effective and compliant with the enterprise privacy management program. An audit can also identify any gaps or weaknesses in the privacy practices and provide recommendations for improvement. An audit can be conducted internally or externally, depending on the scope, objectives, and standards of the audit. References: : CDPSE Review Manual (Digital Version), page 83

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

The proportionality of the data collected for the intended purpose is the most important privacy consideration when developing a contact tracing application. This means that the application should only collect the minimum amount of personal data necessary to achieve the specific and legitimate purpose of preventing and controlling the spread of COVID-191. The application should also ensure that the data collected are relevant, adequate, and not excessive in relation to the purpose2. The application should avoid collecting or processing any data that are not essential for the purpose, such as location data, biometric data, or health data unrelated to COVID-193. The application should also respect the data minimization principle, which requires that the data are kept for no longer than necessary for the purpose4. References:

• European Data Protection Board Guidelines 04/2020 on the use of location data and contact tracing tools in the context of the COVID-19 outbreak
• Article 5(1)© of the General Data Protection Regulation (GDPR)
• Article 29 Data Protection Working Party Opinion 04/2017 on the Proposed Regulation for the ePrivacy Regulation
• Article 5(1)(e) of the GDPR

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

The greatest benefit of adopting data minimization practices is that the associated threat surface is reduced. Data minimization is a privacy principle that states that personal data should be adequate, relevant, and limited to what is necessary for the purposes for which they are processed. Data minimization helps to protect data privacy by reducing the amount and type of personal data that are collected, stored, processed, or shared by an organization. This in turn reduces the exposure of personal data to potential threats, such as unauthorized access, use, disclosure, modification, or loss. References: : CDPSE Review Manual (Digital Version), page 29

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

The best testing method to identify and review the application’s runtime modules is dynamic application security testing (DAST). DAST is a testing technique that analyzes the application’s behavior and functionality during its execution. DAST can detect security and privacy vulnerabilities that are not visible in the source code, such as injection attacks, cross-site scripting, broken authentication, sensitive data exposure, or improper error handling. DAST can also simulate real-world attacks and test the application’s response and resilience. DAST can provide a comprehensive and realistic assessment of the application’s security and privacy posture in the pre-production environment. References:

• [ISACA Glossary of Terms]
• [OWASP Top 10 Web Application Security Risks]
• [ISACA CDPSE Review Manual, Chapter 2, Section 2.4.2]
• [ISACA Journal, Volume 6, 2018, “Dynamic Application Security Testing”]

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.”

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.

References:

• ISACA Certified Data Privacy Solutions Engineer Study Guide, Domain 3: Privacy Engineering, Task 3.4: Implement privacy engineering techniques to protect data in applications and systems, p. 106-107.
• What Is an API Key? | API Key Definition | Fortinet

Application hardening measures are the most important action to protect a mobile banking app and its data against manipulation and disclosure because they prevent attackers from reverse engineering, tampering, or injecting malicious code into the app. Application hardening measures include techniques such as code obfuscation, encryption, integrity checks, anti-debugging, and anti-tampering mechanisms. These measures make the app more resilient and secure against various types of cyberattacks.

References:

• ISACA Certified Data Privacy Solutions Engineer Study Guide, Domain 3: Privacy Engineering, Task 3.4: Implement privacy engineering techniques to protect data in applications and systems, p. 104-105.
• What is Application Hardening? | Glossary | Digital.ai