Explanation
Data loss prevention (DLP) tools are technologies that help to prevent unauthorized access, use, or transfer of personal data. 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.
References:
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

Explanation
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.
References: Open APIs and Security Risks | Govenda Board Portal Software, The top API security risks and how to mitigate them - Appinventiv, Critical API security risks: 10 best practices | TechBeacon

Explanation
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)

Explanation
A data use policy is a document that defines the rules and guidelines for how personal data are collected, used, stored, shared and deleted by an organization. It is an important part of data governance and compliance, as it helps to ensure that personal data are handled in a lawful, fair and transparent manner, respecting the rights and preferences of data subjects. A data use policy should include the requirements for collecting and using personal data, such as the legal basis, the purpose, the scope, the consent, the data minimization, the accuracy, the security and the accountability. These requirements help to establish the legitimacy and necessity of data processing activities, and to prevent unauthorized or excessive use of personal data.
References:
ISACA Privacy Notice & Usage Disclosures, section 2.1: "We collect Personal Information from you when you provide it to us directly or through a third party who has assured us that they have obtained your consent." Chapter Privacy Policy - Singapore Chapter - ISACA, section 2: "We will collect your personal data in accordance with the PDPA either directly from you or your authorized representatives, and/or through our third party service providers." Data Minimization-A Practical Approach - ISACA, section 2: "Enterprises may only collect as much data as are necessary for the purposes defined at the time of collection, which may also be set out in a privacy notice (sometimes referred to as a privacy statement, a fair processing statement or a privacy policy)." Establishing Enterprise Roles for Data Protection - ISACA, section 3: "Data governance is typically implemented in organizations through policies, guidelines, tools and access controls."

Explanation
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.
99-100 References: : CDPSE Review Manual (Digital Version)