Free and Premium Isaca CDPSE Dumps Questions Answers

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.

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)

Privacy by design is an approach that embeds privacy principles and considerations into the design and development of products, services, systems, and processes that involve personal data. Privacy by design aims to protect the privacy and security of the data subjects, as well as to comply with the applicable privacy laws and regulations. One of the key principles of privacy by design is to obtain the consent and choice of the data subjects regarding the collection, use, and disclosure of their personal data. Therefore, the best example of privacy by design in the development of a consumer mobile application is to require consent before sharing locations, as this gives the data subjects control and transparency over their personal data. The other options are not as effective or sufficient as requiring consent before sharing locations, as they do not address the principle of consent and choice, or they may violate other privacy principles or requirements.

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

ISACA CDPSE Review Manual, Chapter 4, Section 4.3.2

ISACA Journal, Volume 4, 2019, “Tabletop Exercises: Three Sample Scenarios”

ISACA Journal, Volume 6, 2017, “Privacy Breach Response: Preparing for the Inevitable”

Ensuring appropriate data classification should be done next after a migration of personal data involving a data source with outdated documentation has been approved by senior management, 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. Data classification also facilitates the data discovery, data minimization, data retention, and data disposal processes15. References: 1 Domain 3, Task 2; 5 Page 9

Pseudonymization is a technique that replaces or removes direct identifiers from personal data, such as names, addresses, or social security numbers, with pseudonyms, such as codes, tokens, or random values. However, pseudonymization does not eliminate the possibility of re-identification, as the original data can still be linked back to the pseudonyms using additional information or techniques. Therefore, if a database containing pseudonymized personal data is breached, the IT privacy practitioner should be most concerned about the risk of re-identification, which could compromise the privacy and security of the data subjects. The other options are less relevant or important than the risk of re-identification.

The best answer is D. Assess the organization’s exposure related to the migration.

A comprehensive explanation is:

Before an organization migrates data from an on-premise solution to a cloud-hosted solution that spans more than one jurisdiction, it should first assess its exposure related to the migration. This means that the organization should identify and evaluate the potential risks and benefits of moving its data to the cloud, taking into account the legal, regulatory, contractual, and ethical obligations and implications of doing so.

Some of the factors that the organization should consider in its assessment are:

The nature, sensitivity, and value of the data being migrated, and the impact of its loss, theft, corruption, or disclosure on the organization and its stakeholders.

The security, privacy, and compliance requirements and standards that apply to the data in each jurisdiction where it is stored, processed, or accessed, and the differences or conflicts among them.

The trustworthiness, reliability, and reputation of the cloud service provider and its subcontractors, and the terms and conditions of their service level agreements (SLAs) and contracts.

The availability, performance, scalability, and cost-effectiveness of the cloud-hosted solution compared to the on-premise solution, and the trade-offs involved.

The technical feasibility and complexity of migrating the data from the on-premise solution to the cloud-hosted solution, and the tools and methods needed to do so.

The organizational readiness and capability to manage the change and transition from the on-premise solution to the cloud-hosted solution, and the training and support needed for the staff and users.

By conducting a thorough assessment of its exposure related to the migration, the organization can make an informed decision about whether to proceed with the migration or not, or under what conditions or modifications. The assessment can also help the organization to plan and implement appropriate measures and controls to mitigate or avoid any negative consequences and enhance or maximize any positive outcomes of the migration.

Ensuring data loss prevention (DLP) alerts are turned on (A), encrypting the data while it is being migrated (B), and conducting a penetration test of the hosted solution © are all good practices to protect data privacy and security when migrating data from an on-premise solution to a cloud-hosted solution that spans more than one jurisdiction. However they are not the first steps that should be done before the migration. They are more relevant during or after the migration process. They also do not address other aspects of exposure related to the migration, such as legal, regulatory, contractual, or ethical issues.

The data protection principle that is applied when an online business posts its customer data protection notice that includes a statement indicating information is collected on how products are used, the content viewed, and the time and duration of online activities is lawfulness and fairness. Lawfulness and fairness are two of the core principles of data protection under various laws and regulations, such as the GDPR or the CCPA. They state that personal data should be processed lawfully, fairly and in a transparent manner in relation to the data subject. By posting a customer data protection notice that informs customers about what information is collected and for what purpose, the online business demonstrates its compliance with these principles.

System use requirements, data integrity and confidentiality, or data use limitation are not the correct names of the data protection principles that are applied in this case. System use requirements are not a specific principle of data protection, but rather a general term that refers to the rules or policies that govern how users can access and use a system or service. Data integrity and confidentiality are two aspects of the security principle of data protection, which states that personal data should be processed in a manner that ensures appropriate security of the personal data. Data use limitation is not a specific principle of data protection either, but rather a concept that relates to the purpose limitation principle, which states that personal data should be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes.

Risk transfer means shifting financial liability or impact to another party. Cyber insurance (B) directly achieves this by covering breach-related costs. Adopting standards (A) and retaining third parties (C, D) are risk mitigation/reduction, not transfer.

“Risk transfer is commonly achieved via insurance coverage for breach costs and liabilities.”

Conducting a privacy impact assessment (PIA) should be done first to establish privacy by design when developing a contact-tracing application. 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 development and ensures compliance with legal and regulatory requirements. Conducting a development environment review, identifying privacy controls, or identifying differential privacy techniques are important steps in privacy by design, but they should be done after conducting a PIA. References: CDPSE Exam Content Outline, Domain 2, Task 2.1

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

The best way to address privacy concerns when an organization captures personal data from a third party through an open application programming interface (API) is to obtain consent from the data subjects. Consent is a freely given, specific, informed, and unambiguous indication of the data subject’s wishes by which they agree to the processing of their personal data by the organization for a defined purpose. Consent is one of the legal bases for processing personal data under various privacy laws and regulations such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). Obtaining consent from the data subjects can help ensure that they are aware of and agree to the collection and use of their personal data by the organization through the open API. Obtaining consent can also help respect the data subject’s rights and preferences regarding their personal data.

Developing a service level agreement (SLA) with the third party, implementing encryption for the data transmission, or reviewing the specification document of the open API are also good practices for addressing privacy concerns when using an open API to capture personal data from a third party, but they are not the best way. Developing an SLA with the third party can help define the roles, responsibilities, expectations, and obligations of both parties regarding the provision and use of the open API and the personal data involved. Implementing encryption for the data transmission can help protect the confidentiality, integrity, and availability of the personal data transferred between the third party and the organization through the open API. Reviewing the specification document of the open API can help understand the functionality, features, parameters, or requirements of the open API and how it handles personal data.

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. References: 2 Domain 2, Task 9; 4 

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.

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.

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.

The most important consideration for determining the operational life of an encryption key is the volume and sensitivity of data protected by the key. The operational life of an encryption key is the period of time during which the key can be used securely and effectively to encrypt and decrypt data. The operational life of an encryption key depends on various factors, such as the length and complexity of the key, the strength and speed of the encryption algorithm, the number and frequency of encryption operations, the number of entities involved in communication, and the number of digitally signed documents in force. However, among these factors, the volume and sensitivity of data protected by the key is the most critical, as it affects the risk and impact of a potential compromise or exposure of the key. The higher the volume and sensitivity of data protected by the key, the shorter the operational life of the key should be, as this reduces the window of opportunity for an attacker to access or misuse the data.

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.

Collecting more data than necessary for recruitment violates the principle of data minimization, which requires limiting collection to what is adequate, relevant, and necessary. Transparency (A) deals with notice, localization (B) concerns storage jurisdiction, and quality (D) addresses accuracy.

“Data minimization: collect and process only the data that is necessary for the stated purpose.”

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.

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

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.

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.

A data dictionary is a document that defines and describes the data elements, attributes, formats, sources, destinations, purposes and relationships of a data set or system. A data dictionary would be the best way to ensure personal data usage is standardized across the entire organization, as it would provide a common and consistent understanding and reference for how personal data is collected, used, disclosed and transferred within and outside the organization. A data dictionary would also help to ensure compliance with privacy principles, such as accuracy, transparency and accountability. The other options are not as effective as developing a data dictionary in ensuring personal data usage is standardized across the entire organization. De-identify all data is a technique that removes or modifies direct and indirect identifiers in a data set to prevent or limit the identification of the data subjects, but it does not ensure standardization or consistency of personal data usage across the organization. Encrypt all sensitive data is a technique that transforms plain text data into cipher text using an algorithm and a key, making it unreadable by unauthorized parties, but it does not ensure standardization or consistency of personal data usage across the organization. Perform data discovery is a process of identifying and locating personal data within an organization’s systems, databases, applications or files, but it does not ensure standardization or consistency of personal data usage across the organization1, p. 69-70 References: 1: CDPSE Review Manual (Digital Version)

Data sanitization is a process of permanently erasing or destroying data from a storage device or media to prevent unauthorized access or recovery of the data. Data sanitization methods can include physical destruction, degaussing, overwriting, encryption or cryptographic erasure. The most important consideration when using advanced data sanitization methods to ensure privacy data will be unrecoverable is the type of media on which the data is stored, as different media types may require different methods or techniques to achieve effective sanitization. For example, physical destruction may be suitable for optical disks or tapes, but not for solid state drives (SSDs) or flash memory devices. Degaussing may be effective for magnetic disks or tapes, but not for optical disks or SSDs. Overwriting may work for hard disk drives (HDDs) or SSDs, but not for tapes or optical disks. Encryption or cryptographic erasure may be applicable for any media type, but may require additional security measures to protect the encryption keys or certificates. The other options are not as important as the type of media when using advanced data sanitization methods. Subject matter expertise may be helpful, but not essential, as long as the appropriate method is selected and applied correctly. Regulatory compliance requirements may influence the choice of method, but not necessarily determine it, as different methods may meet different standards or criteria. Location of data may affect the feasibility or cost of applying a method, but not its effectiveness or suitability., p. 93-94 References: : CDPSE Review Manual (Digital Version)

The best way to ensure an organization’s enterprise risk management (ERM) framework can protect the organization from privacy harms is to complete a privacy risk assessment. A privacy risk assessment is a systematic process of identifying, analyzing, evaluating, and treating the privacy risks that may affect the organization’s objectives, operations, stakeholders, and reputation. A privacy risk assessment helps to align the ERM framework with the privacy requirements, expectations, and obligations of the organization, as well as to prioritize and mitigate the privacy risks that may cause privacy harms. Privacy harms are the adverse consequences or impacts that may result from the unauthorized or inappropriate use, disclosure, or loss of personal data, such as financial loss, identity theft, discrimination, reputational damage, emotional distress, or physical harm.

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

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.

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. References: : CDPSE Review Manual (Digital Version), page 97

The strategic goals of the organization should be established first before a privacy office starts to develop a data protection and privacy awareness campaign, because they provide the direction, purpose, and scope of the campaign. The strategic goals of the organization reflect its vision, mission, values, and objectives, as well as its alignment with the relevant privacy laws and regulations, stakeholder expectations, and industry best practices. The privacy office should design and implement the awareness campaign in a way that supports and promotes the strategic goals of the organization, as well as measures and evaluates its effectiveness and impact.

The best approach for a local office of a global organization faced with multiple privacy-related compliance requirements is to focus on the requirements with the highest organizational impact, because this will help prioritize the most critical and urgent privacy issues and risks that may affect the organization’s reputation, operations, or legal obligations. Focusing on the highest impact requirements will also help allocate the resources and efforts more efficiently and effectively, as well as align the local office’s privacy practices with the global organization’s objectives and strategies12.

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.

The notice provided to customers during data collection is the most important consideration when determining retention periods for personal data, as it reflects the transparency and accountability principles of privacy and the expectations and preferences of the data subjects. The notice should inform the customers about the purposes and legal bases of the data processing, the rights and choices of the customers, and the safeguards and measures to protect the data, including how long the data will be kept and when it will be deleted or disposed of. The notice should also be consistent with the applicable laws and regulations that may prescribe or limit the retention periods for certain types of personal data. The other options are not as important as the notice provided to customers during data collection when determining retention periods for personal data. Sectoral best practices for the industry may provide some guidance or benchmarks for retention periods, but they may not reflect the specific context or needs of the organization or the customers. Data classification standards may help to categorize data according to its sensitivity and value, but they may not indicate how long the data should be retained or deleted. Storage capacity available for retained data may affect the feasibility or cost of retaining data, but it should not determine or override the retention periods based on privacy principles, laws or customer expectations1, p. 99-100 References: 1: CDPSE Review Manual (Digital Version)

Transport Layer Security (TLS) is a protocol that provides secure communication over the internet by encrypting and authenticating data. TLS provides data integrity through the calculation of message digests, which are cryptographic hashes that summarize the content and structure of a message. The sender and the receiver of a message can compare the message digests to verify that the message has not been altered or corrupted during transmission. TLS also uses digital certificates, asymmetric encryption, and symmetric encryption to provide confidentiality and authentication, but these are not directly related to data integrity.

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

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

Before an organization can respond to data subject access requests (DSARs), it needs to have a clear understanding of the data in its possession, such as what types of personal data are collected, where they are stored, how they are processed, who has access to them, and how long they are retained. This will help the organization to locate and retrieve the relevant data for each DSAR, and to ensure that the data are accurate, complete and up to date. Understanding the data in its possession will also help the organization to comply with other data protection principles and obligations, such as data minimization, purpose limitation, security and accountability.

The other options are less important or irrelevant to do first. Investing in a platform to automate data review may help to speed up the response process, but it does not guarantee that the organization has identified all the data sources and categories that are subject to DSARs. Confirming what is required for disclosure is also important, but it depends on the specific request and the applicable law or regulation. Creating a policy for handling access requests is a good practice, but it should be based on a thorough understanding of the data in its possession.

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)

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

The date and time of the event

The identity of the user or system that performed the event

The type and description of the event

The outcome or result of the event

The personal data that were accessed, used, modified or deleted

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

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

HTTP cookies are the standard mechanism used by websites to recognize returning visitors by storing a small piece of metadata on the user’s browser. Flash cookies (D) and supercookies (A) exist but are not as common or compliant. Server cookies (C) are not a distinct browser-side identifier. The CDPSE emphasizes cookies as central to tracking, profiling, and consent requirements.

“Cookies… small data stored on a browser to recognize repeat visits and enable tracking.”

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.

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)

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 is the best method to protect customers’ personal data that is forwarded to a central system for analysis, as it reduces the linkability of the data set with the original identity of the customers and thus enhances the privacy and security of the data. Pseudonymization also preserves some characteristics or patterns of the original data that can be used for analysis or research purposes, without compromising the accuracy or quality of the results. The other options are not as effective as pseudonymization in protecting customers’ personal data that is forwarded to a central system for analysis. Deletion is a technique that removes or destroys data from a storage device or media to prevent unauthorized access or recovery of the data, but it does not allow for any analysis or research purposes. 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 reduce the linkability of the data set with the original identity of the customers and may require additional security measures to protect the encryption keys or certificates. Anonymization is a technique that removes or modifies all identifiers in a data set to prevent or limit the identification of the data subjects, but it may affect the accuracy or quality of the analysis or research results, as some characteristics or patterns of the original data may be lost or distorted1, p. 74-75 References: 1: CDPSE Review Manual (Digital Version)

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

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

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)

The data controller determines the purposes and means of processing and therefore retains primary accountability for risk—even if a processor or custodian executes processing. Data processors (C) act under instructions; custodians (A) safeguard data but do not define processing; data scientists (B) are operational users, not accountable owners.

“The data controller remains accountable for compliance and risk regardless of outsourcing processing activities.”

Mandatory access control (MAC) is a security model where access to data is determined by labels and clearances. In a data warehouse, MAC can be used to mask or hide specific data within a larger database based on a user's security level. This ensures that users only see the data they are authorized to access.

Data flow diagrams (DFDs) are most valuable for illustrating the locations and movements of personal data across systems and processes, which is essential for compliance mapping and controls. Mapping at rest (C) or transit (D) are subsets of this broader view, while (A) is vague.

“DFDs identify where personal data resides and flows, supporting compliance and risk management.”

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

It can hide the identity and location of the peers, by using encryption, pseudonyms, proxies, or onion routing techniques, such as Tor1 or I2P2. These techniques can prevent eavesdropping, tracking, or censorship by third parties, such as Internet service providers, governments, or hackers.

It can distribute the data across multiple peers, by using hashing, replication, or fragmentation techniques, such as BitTorrent3 or IPFS4. These techniques can reduce the risk of data loss, corruption, or tampering by malicious peers, and increase the availability and resilience of the data.

It can enable the peers to control their own data, by using consensus, validation, or incentive mechanisms, such as blockchain5 or smart contracts. These mechanisms can ensure the integrity and authenticity of the data transactions, and enforce the privacy policies and preferences of the data owners.

The need-to-know basis principle is a security principle that states that access to personal data should be limited to those who have a legitimate purpose for accessing it. The need-to-know basis principle helps to protect data privacy by minimizing the exposure of personal data to unauthorized or unnecessary parties, reducing the risk of data breaches, leaks, or misuse. The need-to-know basis principle should be applied when designing a role-based user access model for a new application, by defining clear roles and responsibilities for different users, granting access rights based on their roles and functions, and enforcing access controls and audits to monitor and verify data access. References: : CDPSE Review Manual (Digital Version), page 105

Before any processing, CDPSE stresses lawfulness: identify and document the appropriate legal basis and processing purpose(s). Security controls (C), algorithmic techniques (B), and aggregation (D) are important but secondary to establishing a lawful basis and purpose limitation.

Key CDPSE-aligned phrasing (short extract): “Processing requires a lawful basis and defined purposes prior to collection/use.”

Storing tokens of PII directly in database fields undermines the security of tokenization and risks re-identification, making it the most concerning issue. Shared drives (A) and lack of labels (B) are governance gaps, and limited third-party access (C) can be controlled contractually, but token misuse (D) poses direct privacy risk.

“Improper token storage can compromise de-identification, reintroducing privacy risk.”

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)

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

[ISACA Glossary of Terms]

[OWASP Top 10 Web Application Security Risks]

[ISACA CDPSE Review Manual, Chapter 2, Section 2.4.2]

[ISACA Journal, Volume 6, 2018, “Dynamic Application Security Testing”]

The best way to safeguard the encryption keys is to ensure that they are stored in a cryptographic vault. A cryptographic vault is a secure hardware or software module that provides cryptographic services and protects the keys from unauthorized access, modification, or disclosure. A cryptographic vault can also provide other functions, such as key generation, key backup, key rotation, key destruction, and key auditing. A cryptographic vault can enhance the security and privacy of the encrypted data by preventing key compromise, leakage, or misuse. A cryptographic vault can also comply with the security standards and best practices for key management, such as the ISO/IEC 27002, NIST SP 800-57, or PCI DSS. References:

[ISACA Glossary of Terms]

[ISACA CDPSE Review Manual, Chapter 3, Section 3.3.3]

[ISACA Journal, Volume 4, 2019, “Key Management in the Multi-Cloud Environment”]

[ISACA CDPSE Review Manual, Chapter 3, Section 3.3.4]

The data subject should contact the organization’s chief privacy officer (CPO) first if they believe their personal information has been collected and used without consent. The CPO is the senior executive who is responsible for establishing and maintaining the organization’s privacy vision, strategy, and program. The CPO oversees the development and implementation of privacy policies, procedures, standards, and controls, and ensures that they align with the organization’s business objectives and legal obligations. The CPO also leads the privacy governance structure, such as the privacy steering committee, and coordinates with other stakeholders, such as the data protection authorities, the privacy rights advocates, and the outside privacy counsel, to ensure that privacy is integrated into all aspects of the organization’s operations. The CPO is the primary point of contact for data subjects who have any questions, complaints, or requests regarding their personal information, and who can address their concerns and resolve their issues in a timely and effective manner. References: : CDPSE Review Manual (Digital Version), page 21

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)

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

Sectoral is a privacy protection reference model that refers to a system of laws and regulations that apply to specific sectors or industries within a jurisdiction, such as health, finance, education or telecommunications. Sectoral privacy protection is typically characterized by having different rules and standards for different types of personal data or data processing activities, depending on the sensitivity and value of the data or the impact and risk of the processing. When a government’s health division established the complete privacy regulation for only the health market, it is using a sectoral privacy protection reference model, as it is addressing the specific needs and challenges of the health sector in terms of privacy protection. The other options are not applicable in this scenario. Co-regulatory is a privacy protection reference model that refers to a system of laws and regulations that are supplemented by self-regulation mechanisms, such as codes of conduct, standards or certification schemes, developed by industry associations or professional bodies with oversight from government agencies or regulators. Comprehensive is a privacy protection reference model that refers to a system of laws and regulations that apply to all sectors and industries within a jurisdiction, regardless of the type or nature of personal data or data processing activities. Self-regulatory is a privacy protection reference model that refers to a system of laws and regulations that rely on voluntary compliance by organizations with their own policies and procedures, without any external oversight or enforcement from government agencies or regulators1, p. 63-64 References: 1: CDPSE Review Manual (Digital Version)

The principle of data minimization 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 using only the data required by the application, the organization can reduce the amount of data that is collected, stored, processed and potentially exposed. This can also help the organization comply with privacy laws and regulations that require data minimization, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

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)

A data inventory is a comprehensive list of the data that an organization collects, processes, stores, transfers, and disposes of. It includes information such as the type, source, location, owner, purpose, and retention period of the data. A data inventory is essential for understanding where personal data is coming from and how it is used within the organization, as well as for complying with data privacy laws and regulations. A data inventory also helps to identify and mitigate data privacy risks and gaps.

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

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

Consent tagging is a metadata-driven process that associates consent preferences with an individual’s data, enabling organizations to manage consent dynamically across systems. It is not limited to cookies (A), one-time logging (C), or initial requests (D).

“Consent tagging links an individual’s data with their recorded consent choices for compliant processing.”

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.