CDPSE Certified Data Privacy Solutions Engineer Questions and Answers

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

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

References:

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

When contracting with a SaaS provider, it is important to ensure that the provider will remove all customer data from their systems and storage devices at the end of the service contract. This will prevent any unauthorized access, use, or disclosure of the customer data by the provider or third parties after the service termination. Removal of customer data means that the data are permanently erased and cannot be recovered or restored by any means.

References:

• ISACA, Data Privacy Audit/Assurance Program, Control Objective 9: Data Disposal, p. 16-171
• ISACA, CDPSE Review Manual 2021, Chapter 4: Privacy Incident Response, Section 4.2: Data Disposal and Destruction, p. 151-152.

The vulnerability that would have the greatest impact on the privacy of information is private key exposure, because it would compromise the encryption and decryption of the information, as well as the authentication and integrity of the communicating parties. A private key is a secret and unique value that is used to encrypt or decrypt data, or to sign or verify digital signatures. If an attacker gains access to the private key, they can read, modify, or impersonate the data or the sender, which would violate the confidentiality, integrity, and authenticity of the information12.

References:

• CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.3 – Privacy Architecture Implementation3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 – Privacy Architecture, Section 2.4 – Remote Access4.

Data privacy standards are the set of rules, guidelines, and best practices that define the requirements and expectations for the collection, processing, storage, sharing, and disposal of personal data. Data privacy standards help to ensure that personal data is treated in a fair, lawful, transparent, and secure manner, as well as to comply with the applicable privacy laws and regulations. Data privacy standards also help to define the data retention time in a stream-fed data lake that includes personal data, as they specify the criteria and conditions for how long personal data can be kept in the data lake, based on factors such as the purpose, necessity, relevance, and quality of the data. Data retention time is an important aspect of data privacy, as it affects the risk of data breaches, unauthorized access, or misuse of personal data.

References: CDPSE Review Manual, 2021, p. 80

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 privacy impact assessment (PIA) is a systematic process of identifying and evaluating the potential privacy risks and impacts of a data processing activity or system. A PIA helps to ensure that privacy is considered and integrated into the design and development of data processing activities or systems, and that privacy risks are mitigated or eliminated. A PIA also helps to determine the appropriate retention periods for personal data based on the purpose and necessity of the data processing, as well as the legal and regulatory obligations that apply to the data. Therefore, a PIA helps to define data retention time in a stream-fed data lake that includes personal data. References: : CDPSE Review Manual (Digital Version), page 99

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.

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

References:

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

The most important consideration when writing an organization’s privacy policy is to align the statements to the organizational practices, because this will help ensure that the policy is accurate, consistent, and transparent. A privacy policy is a document that explains how the organization collects, uses, discloses, and protects personal data from its customers, employees, partners, and other stakeholders. A privacy policy should reflect the actual data processing activities and privacy measures of the organization, as well as comply with the applicable laws and regulations. A privacy policy that is not aligned with the organizational practices may lead to confusion, mistrust, or legal liability12.

References:

• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.2 – Privacy Policy3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 1 – Privacy Governance, Section 1.2 – Data Privacy Laws and Regulations4.

From a privacy perspective, it is most important to ensure data backups are encrypted. Encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Encryption can help protect the confidentiality, integrity, and availability of data backups by preventing unauthorized access, disclosure, or modification. Encryption can also help comply with legal and regulatory requirements for data protection, such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Encryption can be applied to data backups at different levels, such as file-level, disk-level, or network-level encryption.

Incremental backups, differential backups, or pseudonymization are also useful for data backup management, but they are not the most important from a privacy perspective. Incremental backups are backups that only copy the data that has changed since the last backup, whether it was a full, differential, or incremental backup. Incremental backups can help save storage space and time, but they do not directly protect the data from unauthorized access or disclosure. Differential backups are backups that only copy the data that has changed since the last full backup. Differential backups can also help save storage space and time, but they also do not directly protect the data from unauthorized access or disclosure. Pseudonymization is a process of replacing identifying information in data with artificial identifiers or pseudonyms. Pseudonymization can help enhance the privacy of data by reducing the linkability between data and data subjects, but it does not prevent re-identification or inference attacks.

References: Data backups 101: A complete guide for 2023 - Norton, Backup & Secure | U.S. Geological Survey - USGS.gov, The GDPR: How the right to be forgotten affects backups

The scenario that poses the greatest risk to an organization from a privacy perspective is that the organization lacks a hardware disposal policy. A hardware disposal policy is a policy that defines how the organization should dispose of or destroy hardware devices that contain or process personal data, such as laptops, servers, hard drives, USBs, etc. A hardware disposal policy should ensure that personal data is securely erased or overwritten before the hardware device is discarded, recycled, donated, or sold. A hardware disposal policy should also comply with the applicable privacy regulations and standards that govern data retention and destruction. By lacking a hardware disposal policy, the organization exposes personal data to potential threats, such as theft, loss, or unauthorized access, use, disclosure, or transfer. References: : CDPSE Review Manual (Digital Version), page 123

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

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

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

Public key infrastructure (PKI) is a system that enables the use of public key cryptography, which is a method of encrypting and authenticating data using a pair of keys: a public key and a private key. Public key cryptography can protect against man-in-the-middle (MITM) attacks, which are attacks where an attacker intercepts and modifies the communication between two parties. PKI makes public key cryptography feasible by providing a way to generate, distribute, verify, and revoke public keys. PKI also uses digital certificates, which are documents that bind a public key to an identity, and certificate authorities, which are trusted entities that issue and validate certificates. By using PKI, the parties can ensure that they are communicating with the intended recipient and that the data has not been tampered with by an attacker.

References:

• What is Public Key Infrastructure (PKI)? - Fortinet
• How is man-in-the-middle attack prevented in TLS? [duplicate]
• A brief look at Man-in-the-Middle Attacks and the Role of Public Key Infrastructure (PKI)

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

References:

• CDPSE Exam Content Outline, Domain 1 – Privacy Governance (Governance, Management & Risk Management), Task 1: Identify issues requiring remediation and opportunities for process improvement1.
• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.3 – Privacy Impact Assessment (PIA)2.

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)

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

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

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

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 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

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

References: CDPSE Review Manual, 2021, p. 149

The 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

Data flows are the most important to review before using an application programming interface (API) to help mitigate related privacy risk. Data flows are the paths or routes that data take from their sources to their destinations through various processes, transformations, or exchanges. Data flows can help understand how data are collected, used, shared, stored, or deleted by an API and its related applications. Data flows can also help identify the potential privacy risks or impacts that may arise from data processing activities involving an API and its related applications. Data flows can be represented by diagrams, maps, models, or documents that show the sources, destinations, types, formats, volumes, frequencies, purposes, or legal bases of data.

Data taxonomy, data classification, and data collection are also important for privacy risk mitigation when using an API, but they are not the most important. Data taxonomy is a system of organizing and categorizing data into groups, classes, or hierarchies based on their characteristics, attributes, or relationships. Data taxonomy can help understand the structure, meaning, context, or value of data. Data classification is a process of assigning labels or tags to data based on their sensitivity, confidentiality, criticality, or risk level. Data classification can help determine the appropriate level of protection or handling for data. Data collection is a process of gathering or obtaining data from various sources for a specific purpose or objective. Data collection can help obtain the necessary information or evidence for decision making or problem solving.

References: Critical API security risks: 10 best practices | TechBeacon, Open APIs and Security Risks | Govenda Board Portal Software, The top API security risks and how to mitigate them - Appinventiv

Pseudonymization is a technique that replaces direct identifiers in a data set with pseudonyms or artificial identifiers that do not reveal the identity of the data subjects. Pseudonymization reduces the linkability of the data set with the original identity of the data subjects and thus enhances the privacy and security of the data. However, pseudonymization is not irreversible and the original identity can be re-established if the pseudonym or key is compromised. Therefore, it is important to keep the identifier separate and distinct from the data it protects and to apply additional security measures to safeguard the identifier. The other options are not relevant to pseudonymization1, p. 74-75 References: 1: CDPSE Review Manual (Digital Version)

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

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

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

A legal review is the most important thing to ensure before executive leadership approves a new data privacy policy, as it would help to verify and validate the accuracy, completeness and compliance of the policy with the applicable laws and regulations that govern the collection, use, disclosure and transfer of personal data. A legal review would also help to identify and address any gaps, inconsistencies or conflicts in the policy, and to provide legal advice or guidance on the implementation and enforcement of the policy. The other options are not as important as a legal review in ensuring before executive leadership approves a new data privacy policy. A training program is a method of educating and informing the employees and stakeholders about the new data privacy policy, its objectives, requirements and implications, but it does not ensure the quality or compliance of the policy itself. A privacy committee is a group of individuals who are responsible for overseeing, monitoring and evaluating the organization’s data privacy program, policies and practices, but it does not ensure the quality or compliance of the policy itself. A distribution methodology is a method of disseminating and communicating the new data privacy policy to the employees and stakeholders, such as email, intranet, website or newsletter, but it does not ensure the quality or compliance of the policy itself1, p. 98 References: 1: CDPSE Review Manual (Digital Version)

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)

The most effective way to support organizational privacy awareness objectives is D. Customizing awareness training by business unit function.

A comprehensive explanation is:

Organizational privacy awareness objectives are the goals and expectations that an organization sets for its employees and stakeholders regarding the protection and management of personal data. Privacy awareness objectives may vary depending on the nature, scope, and purpose of the organization’s data processing activities, as well as the legal, regulatory, contractual, and ethical obligations and implications that apply to them.

One of the best practices to support organizational privacy awareness objectives is to customize awareness training by business unit function. This means that the organization should design and deliver privacy awareness training programs that are tailored to the specific roles, responsibilities, and needs of each business unit or department within the organization. Customizing awareness training by business unit function can have several benefits, such as:

• Enhancing the relevance and effectiveness of the training content and methods for each audience group, by addressing their specific privacy challenges, risks, and opportunities.
• Increasing the engagement and motivation of the trainees, by showing them how privacy relates to their daily tasks, goals, and performance.
• Improving the retention and application of the training knowledge and skills, by providing practical examples, scenarios, and exercises that reflect the real-world situations and problems that the trainees may encounter.
• Fostering a culture of privacy across the organization, by creating a common language and understanding of privacy concepts, principles, and practices among different business units or departments.

Some examples of how to customize awareness training by business unit function are:

• Providing different levels or modules of training based on the degree of access or exposure to personal data that each business unit or department has. For example, a basic level of training for all employees, an intermediate level of training for employees who handle personal data occasionally or incidentally, and an advanced level of training for employees who handle personal data regularly or extensively.
• Providing different topics or themes of training based on the type or category of personal data that each business unit or department processes. For example, a general topic of training for employees who process non-sensitive or non-personal data, a specific topic of training for employees who process sensitive or special data categories (such as health, biometric, financial, or political data), and a specialized topic of training for employees who process high-risk or high-value data (such as intellectual property, trade secrets, or customer loyalty data).
• Providing different formats or modes of training based on the preferences or constraints of each business unit or department. For example, a face-to-face format of training for employees who work in the same location or office, an online format of training for employees who work remotely or across different time zones, and a blended format of training for employees who work in a hybrid mode or have flexible schedules.

The other options are not as effective as option D.

Funding in-depth training and awareness education for data privacy staff (A) may improve the competence and confidence of the data privacy staff who are responsible for designing and implementing the privacy policies and practices of the organization, but it does not necessarily support the organizational privacy awareness objectives for the rest of the employees and stakeholders.

Implementing an annual training certification process (B) may ensure that the employees and stakeholders are updated and refreshed on the privacy policies and practices of the organization on a regular basis, but it does not necessarily address their specific privacy needs and challenges based on their business unit function.

Including mandatory awareness training as part of performance evaluations © may incentivize the employees and stakeholders to participate in and complete the privacy awareness training programs offered by the organization, but it does not necessarily enhance their understanding and application of privacy concepts and principles based on their business unit function.

References:

• The Benefits of Information Security and Privacy Awareness Training Programs1
• What Is Your Privacy and Data Protection Strategy?2
• What is Data Privacy Awareness?3

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

User 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

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

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

References: Data Minimization | Washington Technology Solutions, What Is Data Minimization? The Principles According to GDPR | 2BAdvice, Data Protection Principles: Core Principles of the GDPR - Cloudian

The first thing to do when a data collection process is deemed to be a high-level risk is to conduct a privacy impact assessment (PIA). A PIA is a systematic process that identifies and evaluates the potential effects of personal data processing operations on the privacy of individuals and the organization. A PIA helps to identify privacy risks and mitigation strategies at an early stage of the data collection process and ensures compliance with legal and regulatory requirements. A PIA also helps to demonstrate accountability and transparency to stakeholders and data subjects regarding how their personal data are collected, used, shared, stored, or deleted.

Performing a business impact analysis (BIA), implementing remediation actions to mitigate privacy risk, or creating a system of records notice (SORN) are also important steps for managing privacy risk, but they are not the first thing to do. Performing a BIA is a process of analyzing the potential impacts of disruptive events on the organization’s critical functions, processes, resources, or objectives. A BIA helps to determine the recovery priorities, strategies, and objectives for the organization in case of a disaster or crisis. Implementing remediation actions is a process of applying corrective or preventive measures to reduce or eliminate the privacy risks identified by the PIA or other methods. Remediation actions may include technical, organizational, or legal solutions, such as encryption, access control, consent management, or contractual clauses. Creating a SORN is a process of publishing a public notice that describes the existence and purpose of a system of records that contains personal data under the control of a federal agency. A SORN helps to inform the public about how their personal data are collected and maintained by the agency and what rights they have regarding their data.

References: Privacy Impact Assessment (PIA) - European Commission, Privacy Impact Assessment (PIA) | ICO, Privacy Impact Assessments | HHS.gov

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

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)

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

Data masking is the process of hiding original data with modified content to protect sensitive data from unauthorized access or disclosure. Data masking is often used for testing purposes in non-production environments, where personal data is not needed or allowed. However, data masking can pose several challenges, especially for a global financial institution that has multiple interconnected systems and applications. One of the greatest challenges is to preserve the complex relationships within and across systems while masking the data. This means that the masked data must maintain the same format, referential integrity, semantic integrity, and uniqueness as the original data, so that the testing results are valid and reliable. For example, if a customer’s name is masked in one system, it must be masked consistently in all other systems that reference it. If a transaction amount is masked in one system, it must not violate any business rules or constraints in another system. If a credit card number is masked in one system, it must still be a valid credit card number in another system. Preserving these complex relationships can be challenging because it requires a thorough understanding of the data model, the business logic, and the dependencies among systems. It also requires a robust and flexible data masking tool that can handle different types of data and platforms.

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)

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

The availability of application data flow diagrams is the most significant factor that impacts an organization’s ability to respond to data subject access requests. Data subject access requests are requests made by data subjects to exercise their rights under privacy laws or regulations, such as the right to access, rectify, erase, or port their personal data. To respond to these requests effectively and efficiently, the organization needs to have a clear and accurate understanding of how personal data is collected, processed, stored, shared, and disposed of within its applications and systems. Application data flow diagrams are graphical representations of the data lifecycle that show the sources, destinations, transformations, and dependencies of the data. Having these diagrams readily available helps the organization to locate, retrieve, modify, or delete the personal data in response to the data subject access requests. The other options are less significant or relevant than the availability of application data flow diagrams, as they do not directly affect the organization’s ability to identify and access the personal data.

References: CDPSE Review Manual, 2021, p. 83

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

Data privacy and data security are related but distinct concepts that are both essential for protecting personal data. Data privacy is about ensuring that personal data are collected, used, shared and disposed of in a lawful, fair and transparent manner, respecting the rights and preferences of the data subjects. Data privacy also involves implementing policies, procedures and controls to comply with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). Data privacy protects users from unauthorized disclosure of their personal data, which may result in harm, such as identity theft, fraud, discrimination or reputational damage.

Data security is about safeguarding the confidentiality, integrity and availability of data from unauthorized or malicious access, use, modification or destruction. Data security also involves implementing technical and organizational measures to prevent or mitigate data breaches or incidents, such as encryption, authentication, backup or incident response. Data security prevents compromise of data, which may result in loss, corruption or disruption of data.

References:

• The Difference Between Data Privacy and Data Security - ISACA, section 1: “Data privacy is focused on the use and governance of personal data—things like putting policies in place to ensure that consumers’ personal information is being collected, shared and used in appropriate ways.”
• Practical Data Security and Privacy for GDPR and CCPA - ISACA, section 1: “Data security is the practice of protecting digital information from unauthorized access, corruption or theft throughout its life cycle.”

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)

A data processor that handles personal data for multiple customers has decided to migrate its data warehouse to a third-party provider. The processor is obligated to seek approval from all in-scope data controllers prior to implementation. A data controller is an entity that determines the purposes and means of processing personal data. A data processor is an entity that processes personal data on behalf of a data controller. A third-party provider is an entity that provides services or resources to another entity, such as a cloud service provider or a hosting provider.

According to various privacy laws and regulations, such as the GDPR or the CCPA, a data processor must obtain explicit consent from the data controller before engaging another processor or transferring personal data to a third country or an international organization. The consent must specify the identity of the other processor or the third country or international organization, as well as the safeguards and guarantees for the protection of personal data. The consent must also be documented in a written contract or other legal act that binds the processor to respect the same obligations as the controller.

Seeking approval from all in-scope data controllers can help ensure that the processor complies with its contractual and legal obligations, respects the rights and preferences of the data subjects, and maintains transparency and accountability for its processing activities.

Obtaining assurance that data subject requests will continue to be handled appropriately, implementing comparable industry-standard data encryption in the new data warehouse, or ensuring data retention periods are documented are also good practices for a data processor that migrates its data warehouse to a third-party provider, but they are not obligations prior to implementation. Rather, they are requirements or recommendations during or after implementation.

Obtaining assurance that data subject requests will continue to be handled appropriately is a requirement for a data processor that processes personal data on behalf of a data controller. Data subject requests are requests made by individuals to exercise their rights regarding their personal data, such as access, rectification, erasure, restriction, portability, or objection. A data processor must assist the data controller in fulfilling these requests within a reasonable time frame and without undue delay.

Implementing comparable industry-standard data encryption in the new data warehouse is a recommendation for a data processor that transfers personal data to another system or location. Data encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Data encryption can help protect the confidentiality, integrity, and availability of personal data by preventing unauthorized access, disclosure, or modification.

Ensuring data retention periods are documented is a requirement for a data processor that stores personal data on behalf of a data controller. Data retention periods are the durations for which personal data are kept before they are deleted or anonymized. Data retention periods must be determined by the purpose and necessity of processing personal data and must comply with legal and regulatory obligations.

References: Data warehouse migration tips: preparation and discovery - Google Cloud, Plan a data warehouse migration - Cloud Adoption Framework, Migrating your traditional data warehouse platform to BigQuery …

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

References:

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

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. 

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]

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

The best way to address the concern that data collected by a third-party vendor and provided back to the organization may not be protected according to the organization’s privacy notice is to validate contract compliance. This means that the organization should verify that the third-party vendor is adhering to the terms and conditions of the contract, which should include clauses on data protection, privacy, and security. The contract should also specify the obligations and responsibilities of both parties regarding data collection, processing, storage, transfer, retention, and disposal. By validating contract compliance, the organization can ensure that the third-party vendor is following the same privacy standards and practices as the organization.

References:

• ISACA, CDPSE Review Manual 2021, Chapter 2: Privacy Governance, Section 2.3: Third-Party Management, p. 51-52.
• ISACA, Data Privacy Audit/Assurance Program, Control Objective 8: Third-Party Management, p. 14-151

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)