CDPSE Exam Dumps - Isaca Certification Questions and Answers

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 most important consideration for developing data retention requirements is the applicable regulations that govern the data. Different types of data may be subject to different legal and regulatory obligations, such as how long the data must be kept, how it must be protected, and how it can be accessed or disposed of. Failing to comply with these obligations can result in fines, penalties, lawsuits, or reputational damage for the organization. Therefore, it is essential to identify and follow the applicable regulations for each data category.

References:

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

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

Endpoint encryption is a security practice that transforms the data stored on a laptop or other device into an unreadable format using a secret key or algorithm. Endpoint encryption protects the privacy of data in case of loss or theft, by ensuring that only authorized parties can access and use the data, while unauthorized parties cannot decipher or modify the data without the key or algorithm. Endpoint 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.

The other options are less effective or irrelevant for protecting the privacy of data stored on a laptop in case of loss or theft. Strong authentication controls, such as passwords, biometrics or multifactor authentication, are important for verifying the identity and access rights of users, but they do not protect the data from being accessed by bypassing or breaking the authentication mechanisms. Remote wipe is a feature that allows users or administrators to erase the data on a lost or stolen device remotely, but it depends on the availability of network connection and device power, and it may not prevent data recovery by sophisticated tools. Regular backups are a process of creating copies of data for recovery purposes, such as in case of data loss or corruption, but they do not protect the data from being accessed by unauthorized parties who may obtain the backup media or files.

References:

• An Ethical Approach to Data Privacy Protection - ISACA, section 2: “Encryption is one of the most effective security controls available to enterprises, but it can be challenging to deploy and maintain across a complex enterprise landscape.”
• How to Protect and Secure Your Data in 10 Ways - TechRepublic, section 1: “Encrypt your hard drive Most work laptops use BitLocker to encrypt local files. That way, if the computer is stolen or hacked, the data it contains will be useless to the malicious actor.”
• 10 Tips to Protect Your Files on PC and Cloud - microsoft.com, section 1: “Encrypt your hard drive Most work laptops use BitLocker to encrypt local files. That way, if the computer is stolen or hacked, the data it contains will be useless to the malicious actor.”
• 11 practical ways to keep your IT systems safe and secure | ICO, section 1: “Use strong passwords and multi-factor authentication Make sure you use strong passwords on smartphones, laptops, tablets, email accounts and any other devices or accounts where personal information is stored.”

In GDPR parlance, organizations that use third-party service providers are often, but not always, considered data controllers, which are entities that determine the purposes and means of the processing of personal data, which can include directing third parties to process personal data on their behalf. The third parties that process data for data controllers are known as data processors.

The best way to protect personal data in the custody of a third party is to include requirements to comply with the organization’s privacy policies in the contract. This means that the organization should specify the terms and conditions of data processing, such as the purpose, scope, duration, and security measures, and ensure that they are consistent with the organization’s privacy policies and applicable privacy regulations. The contract should also define the roles and responsibilities of both parties, such as data controller and data processor, and establish mechanisms for monitoring, reporting, auditing, and resolving any issues or incidents related to data privacy. References: : CDPSE Review Manual (Digital Version), page 41

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

Cloud vendor agreements are the best way to ensure an organization’s data retention requirements will be met in the public cloud environment because they define the roles, responsibilities and obligations of both parties regarding the collection, storage, processing and disposal of data in the cloud. They also specify the terms and conditions for data protection, security, privacy, compliance and auditability12. Data classification schemes, automated data deletion schedules and service level agreements (SLAs) are useful tools to manage and monitor data retention, but they do not guarantee that the cloud vendor will adhere to the organization’s data retention requirements or that they will be enforceable in case of disputes.

References: 1: CDPSE Review Manual (Digital Version), Domain 1: Privacy Governance, Task 1.7: Participate in the management and evaluation of contracts, service levels and practices of vendors and other external parties 2: CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2: Privacy Governance, Section: Vendor Management

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

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

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