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Validation and certification of data destruction is the most important consideration when choosing a method for data destruction, because it provides evidence that the data has been destroyed beyond recovery and that the organization has complied with the applicable information security frameworks and legal requirements. Validation and certification can also help to prevent data breaches, avoid legal liabilities, and enhance the organization’s reputation and trustworthiness. Different methods of data destruction may have different levels of validation and certification, depending on the type of media, the sensitivity of the data, and the standards and guidelines followed. For example, some methods may require a third-party verification or audit, while others may generate a certificate of destruction or a report of erasure. Therefore, the organization should choose a method that can provide sufficient validation and certification for its specific needs and obligations.

References:

Secure Data Disposal and Destruction: 6 Methods to Follow, KirkpatrickPrice

Data Destruction Standards and Guidelines, BitRaser

Best Practices for Data Destruction, U.S. Department of Education

Data masking is the process of hiding original data with modified content to protect sensitive data from unauthorized access or disclosure. Data masking is often used for testing purposes in non-production environments, where personal data is not needed or allowed. However, data masking can pose several challenges, especially for a global financial institution that has multiple interconnected systems and applications. One of the greatest challenges is to preserve the complex relationships within and across systems while masking the data. This means that the masked data must maintain the same format, referential integrity, semantic integrity, and uniqueness as the original data, so that the testing results are valid and reliable. For example, if a customer’s name is masked in one system, it must be masked consistently in all other systems that reference it. If a transaction amount is masked in one system, it must not violate any business rules or constraints in another system. If a credit card number is masked in one system, it must still be a valid credit card number in another system. Preserving these complex relationships can be challenging because it requires a thorough understanding of the data model, the business logic, and the dependencies among systems. It also requires a robust and flexible data masking tool that can handle different types of data and platforms.

Critical data elements are the data elements that are essential for the organization to achieve its business objectives, comply with legal and regulatory requirements, and protect the privacy and security of the data subjects. Critical data elements should be mapped to the data process flow, which is a graphical representation of how data is collected, processed, stored, shared, and disposed of within the organization. Mapping critical data elements to the data process flow helps to identify the sources, destinations, transformations, and dependencies of the data, as well as the potential risks and controls associated with each step of the data lifecycle.

References: CDPSE Review Manual, 2021, p. 83

The right not to be profiled is the right of data subjects to not be subject to a decision based solely on automated processing, including profiling, which produces legal or similarly significant effects on them. The online application that delivers a mortgage decision automatically based on criteria set by the lender is an example of such a decision, as it affects the data subject’s ability to obtain a loan.

References:

What exactly is ‘profiling’ under the GDPR - DMA

Can I be subject to automated individual decision-making, including profiling - European Commission

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

Performing a full wipe and reimage of the laptops is the best control to prevent the exposure of personal information when redeploying laptops within an organization. This is because a full wipe and reimage ensures that all data, including personal information, is securely erased from the laptops and replaced with a fresh installation of the operating system and applications. This reduces the risk of data leakage, unauthorized access, or data recovery by malicious actors or unauthorized users. The other options are not as effective or sufficient as a full wipe and reimage, as they do not guarantee the complete removal of personal information from the laptops.

References: CDPSE Review Manual, 2021, p. 147

The most effective way to incorporate privacy by design principles into applications is to include privacy requirements in software development practices, because this ensures that privacy is considered and integrated from the early stages of the design process and throughout the entire lifecycle of the application. Software development practices include activities such as defining the scope, objectives, and specifications of the application, identifying and analyzing the privacy risks and impacts, selecting and implementing the appropriate privacy-enhancing technologies and controls, testing and validating the privacy functionality and performance, and monitoring and reviewing the privacy compliance and effectiveness of the application. By including privacy requirements in software development practices, the organization can achieve a proactive, preventive, and embedded approach to privacy that aligns with the privacy by design principles.

References:

CDPSE Review Manual, 2023 Edition, Domain 2: Privacy Architecture, Section 2.1.2: Privacy Requirements, p. 75

CDPSE Review Manual, 2023 Edition, Domain 2: Privacy Architecture, Section 2.2.1: Privacy by Design Methodology, p. 79-80

The 7 Principles of Privacy by Design | Blog | OneTrust1

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

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

 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.

References:

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