Data retention is a process of keeping personal data for a specified period of time for legitimate purposes, such as legal obligations, contractual agreements, business operations or historical records. Data retention should be based on the principle of data minimization, which requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. Data retention should also comply with the principle of storage limitation, which requires deleting or disposing of personal data when it is no longer needed or justified. The most likely valid use case for keeping a customer's personal data after contract termination is a required retention period due to regulations, such as tax laws, financial laws, health laws or consumer protection laws, that mandate the organization to retain certain types of customer data for a certain period of time after the end of the contractual relationship. The other options are not valid use cases for keeping a customer's personal data after contract termination, as they do not meet the criteria of necessity, relevance or justification. For the purpose of medical research, the organization would need to obtain the consent of the customer or have another legal basis for processing their personal data for a different purpose than the original contract. A forthcoming campaign to win back customers or ease of onboarding when the customer returns are not legitimate purposes for retaining customer data after contract termination, as they are not related to the original contract and may violate the customer's privacy rights and preferences. , p. 99-100Reference:: CDPSE Review Manual (Digital Version)

The role primarily assigned to an internal data owner is authorizing access rights. A data owner is a person or a role within the organization who has the authority and responsibility for the data assets under their control. A data owner is responsible for defining the data classification, data quality, data retention, and data security requirements for their data assets. A data owner is also responsible for granting, revoking, and reviewing the access rights to their data assets based on the principle of least privilege and the business needs. A data owner is accountable for ensuring that the data assets are used in compliance with the organizational policies and the applicable laws and regulations.Reference:

[ISACA Glossary of Terms]

[ISACA CDPSE Review Manual, Chapter 3, Section 3.2.1]

[ISACA CDPSE Review Manual, Chapter 3, Section 3.2.2]

[ISACA CDPSE Review Manual, Chapter 3, Section 3.2.3]

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.63-64Reference:1: CDPSE Review Manual (Digital Version)

The proportionality of the data collected for the intended purpose is the most important privacy consideration when developing a contact tracing application.This means that the application should only collect the minimum amount of personal data necessary to achieve the specific and legitimate purpose of preventing and controlling the spread of COVID-191.The application should also ensure that the data collected are relevant, adequate, and not excessive in relation to the purpose2.The application should avoid collecting or processing any data that are not essential for the purpose, such as location data, biometric data, or health data unrelated to COVID-193.The application should also respect the data minimization principle, which requires that the data are kept for no longer than necessary for the purpose4.Reference:

European Data Protection Board Guidelines 04/2020 on the use of location data and contact tracing tools in the context of the COVID-19 outbreak

Article 5(1) of the General Data Protection Regulation (GDPR)

Article 29 Data Protection Working Party Opinion 04/2017 on the Proposed Regulation for the ePrivacy Regulation

Article 5(1)(e) of the GDPR

User behavior analytics is a technology that uses data analysis and machine learning to monitor, detect and respond to anomalous or malicious user activities, such as accessing sensitive personal customer information to use for unauthorized purposes. User behavior analytics is the best choice to mitigate this risk, as it would help to identify and prevent insider threats, data breaches, fraud or misuse of data by authorized individuals. User behavior analytics can also help to enforce policies and controls, such as access control, audit trail or data loss prevention. The other options are not as effective as user behavior analytics in mitigating this risk. Email filtering system is a technology that scans and blocks incoming or outgoing emails that contain spam, malware or phishing attempts, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes. Intrusion monitoring is a technology that monitors and alerts on unauthorized or malicious attempts to access a system or network, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes.Mobile device management (MDM) is a technology that manages and secures mobile devices that are used to access or store organizational data, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes1, p.92Reference:1: CDPSE Review Manual (Digital Version)