The primary consideration of a multinational organization deploying a user and entity behavior

analytics (UEBA) tool to centralize the monitoring of anomalous employee behavior is cross-border

data transfer, because it may involve the transfer of personal data across different jurisdictions with

different privacy laws and regulations. The organization needs to ensure that it complies with the

applicable legal requirements and safeguards the privacy rights of its employees when transferring

their data to a central location for analysis. The other options are secondary or operational

considerations that may not have a significant impact on the privacy of the employees.

Reference:

CDPSE Exam Content Outline, Domain 2 – Privacy Architecture (Privacy Architecture

Implementation), Task 3: Implement privacy solutions1.

CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.4 – Cross-Border Data Transfer2.

CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 – Privacy

Architecture, Section 2.5 – Cross-Border Data Transfer3.

User consent to share personal data is the most important factor when designing APIs that enable

mobile device applications to access personal data, as it ensures that the user is informed and agrees

to the purpose, scope, and duration of the data sharing. User consent also helps to comply with the

data protection principles and regulations, such as the General Data Protection Regulation (GDPR)

and the California Consumer Privacy Act (CCPA), that require user consent for certain types of data

processing and sharing134. Reference: 1 Domain 2, Task 7

Public key infrastructure (PKI) is a system that enables the use of public key cryptography, which is a

method of encrypting and authenticating data using a pair of keys: a public key and a private key.

Public key cryptography can protect against man-in-the-middle (MITM) attacks, which are attacks

where an attacker intercepts and modifies the communication between two parties. PKI makes

public key cryptography feasible by providing a way to generate, distribute, verify, and revoke public

keys. PKI also uses digital certificates, which are documents that bind a public key to an identity, and

certificate authorities, which are trusted entities that issue and validate certificates. By using PKI, the

parties can ensure that they are communicating with the intended recipient and that the data has

not been tampered with by an attacker.

Reference:

What is Public Key Infrastructure (PKI)? - Fortinet

How is man-in-the-middle attack prevented in TLS? [duplicate]

A brief look at Man-in-the-Middle Attacks and the Role of Public Key Infrastructure (PKI)

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

Short 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.

Reference:

Privacy Policy Review Checklist, PrivacySense

How to Write a Privacy Policy for Your Website, TermsFeed

The best way to ensure an effective data privacy policy is implemented is to align regulatory

requirements with business needs, because this will help achieve compliance while also supporting

the organization’s objectives, values, and strategies. A data privacy policy should reflect the legal

obligations and expectations of the organization, as well as the needs and p

of its

stakeholders, such as customers, employees, partners, and regulators. A data privacy policy should

also be flexible and adaptable to changing circumstances and environments12.

Reference:

CDPSE Exam Content Outline, Domain 1 – Privacy Governance (Governance, Management & Risk

Management), Task 3: Participate in the evaluation of privacy policies, programs and policies for their

alignment with legal requirements, regulatory requirements and/or industry best practices3.

CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.2 – Privacy Policy4.

An audit log is a record of the activities and events that occur in an information system, such as an

application hosting personal dat

a. 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.

Reference:

IS Audit Basics: Auditing Data Privacy, section 4: “Audit logs should be maintained for all systems that

process PII.”

Data Protection Audit Manual, section 3.2: “Audit trails should be kept for all processing operations

involving personal data.”

Audit Logging Best Practices, section 2: “An audit log entry should contain enough information to

answer who did what and when.”

Data loss prevention (DLP) tools are technologies that help to prevent unauthorized access, use, or

transfer of personal dat

a. DLP tools can monitor, detect, and block data leakage or exfiltration from various sources, such as

endpoints, networks, cloud services, or email. DLP tools can also enforce data protection policies and

compliance requirements, such as encryption, masking, or deletion of sensitive data. DLP tools can

help to protect personal data from both internal and external threats, such as malicious insiders,

hackers, or accidental exposure.

Reference:

Data protection solutions rely on technologies such as data loss prevention (DLP), storage with built-

in data protection, firewalls, encryption, and endpoint protection, Cloudian

Top 10 Hot Data Security And Privacy Technologies, Forbes

A third-party privacy control assessment is an independent and objective evaluation of the design

and effectiveness of the privacy controls implemented by an organization to protect personal data

and comply with privacy laws and regulations. A third-party privacy control assessment can help

senior management to verify the success of its commitment to privacy by design, by providing the

following benefits:

It can measure the extent to which the organization has adopted and integrated the principles and

practices of privacy by design throughout its products, services, processes and systems.

It can identify the strengths and weaknesses of the organization’s privacy governance, policies,

procedures, standards and guidelines, and provide recommendations for improvement.

It can validate the organization’s compliance with the applicable privacy requirements and

expectations of its customers, stakeholders, regulators and auditors.

It can enhance the organization’s reputation and trustworthiness as a responsible and transparent

data controller and processor.

The other options are less effective or irrelevant for verifying the success of the commitment to

privacy by design. Reviewing the findings of an industry benchmarking assessment may provide

some insights into how the organization compares with its peers or competitors in terms of privacy

performance, but it may not reflect the specific privacy goals, risks and challenges of the

organization. Identifying trends in the organization’s amount of compromised personal data or

number of privacy incidents may indicate some aspects of the organization’s privacy maturity, but

they are reactive and lagging indicators that do not capture the proactive and preventive nature of

privacy by design. Moreover, these metrics may not account for other factors that may influence the

occurrence or impact of data breaches or privacy violations, such as external threats, human errors

or environmental changes.

Reference:

Privacy by Design: How Far Have We Come? - ISACA, section 1: “Privacy by design challenges

conventional system thinking. It mandates that any system, process or infrastructure that uses

personal data consider privacy throughout its development life cycle.”

Privacy Control Assessment - ISACA, section 1: “A Privacy Control Assessment (PCA) is an

independent evaluation performed by a qualified assessor to determine whether an entity’s controls

are suitably designed and operating effectively to meet its objectives related to protecting personal

information.”

Privacy by Design: The New Competitive Advantage - ISACA, section 2: “Privacy by design is a

proactive approach to embedding privacy into the design specifications of various technologies,

business practices and networked infrastructure.”

The 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.

Reference: False Positives Handling| Endpoint Data Loss Prevention - ManageEngine …, Scenario-

based troubleshooting guide - DLP Issues, Respond to a DLP policy violation in Power BI - Power BI

Reference: https://www.isaca.org/resources/isaca-journal/issues/2020/volume-4/incidentresponse-models

The primary objective of privacy incident response is to mitigate the impact of privacy incidents on

the organization and the data subjects. Privacy incident response is a process that involves

identifying, containing, analyzing, resolving, and learning from privacy incidents that involve

personal data. Privacy incident response aims to reduce the harm and liability that may result from

privacy incidents, such as reputational damage, regulatory fines, legal actions, or loss of trust. Privacy

incident response also helps to improve the organization’s privacy posture and resilience by

implementing corrective and preventive measures.

While notifying data subjects impacted by privacy incidents may be a legal or ethical obligation, it is

not the primary objective of privacy incident response. Rather, it is one of the possible steps or

outcomes of the process, depending on the nature and severity of the incident. Similarly, reducing

privacy risk to the lowest possible level or optimizing the costs associated with privacy incidents are

desirable goals, but not the main purpose of privacy incident response.

Reference: Privacy incidents and breaches, DHS Privacy Incident Handling Guidance, Incident and

Breach Management