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The best way to ensure data confidentiality across databases is to use data anonymization, which is a process of removing or modifying personal or sensitive data from a dataset so that it cannot be linked or attributed to a specific individual or entity. Data anonymization helps protect the privacy and security of the data subjects, as well as comply with the applicable data protection laws and regulations. Data anonymization can be achieved by using various techniques, such as masking, encryption, aggregation, generalization, perturbation, or synthetic data generation12.

A privacy audit is a systematic and independent examination of an organization’s privacy policies, procedures, practices, and controls to assess their compliance with applicable laws, regulations, standards, and best practices. A privacy audit may result in various outputs, such as findings, recommendations, observations, or opinions. Among the options given, the output that is most likely to trigger remedial action is the identification of deficiencies in how personal data is shared with third parties. This is because such deficiencies may pose significant risks to the privacy and security of the data subjects, as well as to the reputation and legal liability of the organization. Remedial action may include implementing contractual safeguards, technical measures, or organizational changes to ensure that third parties respect and protect the personal data they receive from the organization.

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.

CDPSE/ISACA risk response taxonomy defines risk avoidance as deciding not to engage in the activity that gives rise to the risk. Reduction/mitigation (B/D) means proceed with controls; acceptance (C) means proceed without additional treatment. Not expanding is classic avoidance.

Key CDPSE-aligned phrasing (short extract): “Risk avoidance: Discontinue or do not initiate activities that create risk.”

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]

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. References: 1 Domain 2, Task 7

The first thing that an IT privacy practitioner should do before an organization migrates personal data from an on-premise solution to a cloud-hosted solution is to perform a privacy impact assessment (PIA). A PIA is a systematic process of identifying and evaluating the potential privacy risks and impacts of a data processing activity or system. A PIA helps to ensure that privacy is considered and integrated into the design and development of data processing activities or systems, and that privacy risks are mitigated or eliminated. A PIA also helps to determine the appropriate measures to protect personal data in a cloud-hosted solution, such as encryption, pseudonymization, anonymization, access control, audit trail, breach notification, etc. A PIA also helps to comply with the applicable privacy regulations and standards that govern data processing activities in a cloud-hosted solution. References: : CDPSE Review Manual (Digital Version), page 99

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

Inferred data is the type of data that is produced by using a more complex method of analytics to find correlations between data sets and using them to categorize or profile people. Inferred data is not directly observed or collected from the data subjects, but rather derived from other sources of data, such as behavioral, transactional, or demographic data. Inferred data can be used to make assumptions or predictions about the data subjects’ preferences, interests, behaviors, or characteristics12.

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 preferences 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.