Isaca CDPSE Certified Data Privacy Solutions Engineer Exam Practice Test

Answer : B

Privacy threat modeling is a methodology for identifying and mitigating privacy threats in a software architecture. It helps to ensure that privacy is considered in the design and development of software systems, and that privacy risks are minimized or eliminated. Privacy threat modeling typically involves the following steps: defining the scope and context of the system, identifying the data flows and data elements, identifying the privacy threats and their sources, assessing the impact and likelihood of the threats, and applying appropriate countermeasures to mitigate the threats.Reference:: CDPSE Review Manual (Digital Version), page 97

Answer : B

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.74-75Reference:1: CDPSE Review Manual (Digital Version)

Answer : D

The best course of action to prevent false positives from data loss prevention (DLP) tools is to re-establish baselines for configuration rules. False positives are events that are triggered by a DLP policy in error, meaning that the policy has mistakenly identified non-sensitive data as sensitive or blocked legitimate actions. False positives can reduce the effectiveness and efficiency of DLP tools by generating unnecessary alerts, wasting resources, disrupting workflows, and creating user frustration. To avoid false positives, DLP tools need to have accurate and updated configuration rules that define what constitutes sensitive data and what actions are allowed or prohibited. Configuration rules should be based on clear and consistent criteria, such as data classification levels, data sources, data destinations, data formats, data patterns, user roles, user behaviors, etc. Configuration rules should also be regularly reviewed and adjusted to reflect changes in business needs, regulatory requirements, or threat landscape.

Conducting additional discovery scans, suppressing the alerts generating the false positives, or evaluating new DLP tools are not the best ways to prevent false positives from DLP tools. Conducting additional discovery scans may help identify more sensitive data in the network, but it does not address the root cause of false positives, which is the misconfiguration of DLP policies. Suppressing the alerts generating the false positives may reduce the noise and annoyance caused by false positives, but it does not solve the problem of inaccurate or outdated DLP policies. Evaluating new DLP tools may offer some advantages in terms of features or performance, but it does not guarantee that false positives will be eliminated or reduced without proper configuration and tuning of DLP policies.

Answer : B

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.

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

Answer : A

The organization's potential legal liabilities related to the data should be of greatest concern when an organization wants to store personal data in the cloud, as it may expose the organization to various compliance risks, such as data breach notification laws, data protection regulations, data sovereignty laws, and contractual obligations. The organization should ensure that the cloud storage provider complies with the applicable legal and regulatory requirements, and that the organization retains control and ownership of the data.The organization should also conduct due diligence and risk assessment of the cloud storage provider before entering into a contract.Reference:2Domain 2, Task 9;4

Answer : D

Data privacy and data security are related but distinct concepts that are both essential for protecting personal dat

a. 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.

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.''

Answer : C

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.

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