Explanation
Data flows are the most important to review before using an application programming interface (API) to help mitigate related privacy risk. Data flows are the paths or routes that data take from their sources to their destinations through various processes, transformations, or exchanges. Data flows can help understand how data are collected, used, shared, stored, or deleted by an API and its related applications. Data flows can also help identify the potential privacy risks or impacts that may arise from data processing activities involving an API and its related applications. Data flows can be represented by diagrams, maps, models, or documents that show the sources, destinations, types, formats, volumes, frequencies, purposes, or legal bases of data.
Data taxonomy, data classification, and data collection are also important for privacy risk mitigation when using an API, but they are not the most important. Data taxonomy is a system of organizing and categorizing data into groups, classes, or hierarchies based on their characteristics, attributes, or relationships. Data taxonomy can help understand the structure, meaning, context, or value of data. Data classification is a process of assigning labels or tags to data based on their sensitivity, confidentiality, criticality, or risk level. Data classification can help determine the appropriate level of protection or handling for data. Data collection is a process of gathering or obtaining data from various sources for a specific purpose or objective. Data collection can help obtain the necessary information or evidence for decision making or problem solving.
References: Critical API security risks: 10 best practices | TechBeacon, Open APIs and Security Risks | Govenda Board Portal Software, The top API security risks and how to mitigate them - Appinventiv

Explanation
The best way to ensure an organization's enterprise risk management (ERM) framework can protect the organization from privacy harms is to complete a privacy risk assessment. A privacy risk assessment is a systematic process of identifying, analyzing, evaluating, and treating the privacy risks that may affect the organization's objectives, operations, stakeholders, and reputation. A privacy risk assessment helps to align the ERM framework with the privacy requirements, expectations, and obligations of the organization, as well as to prioritize and mitigate the privacy risks that may cause privacy harms. Privacy harms are the adverse consequences or impacts that may result from the unauthorized or inappropriate use, disclosure, or loss of personal data, such as financial loss, identity theft, discrimination, reputational damage, emotional distress, or physical harm.
References: CDPSE Review Manual, 2021, p. 84

Explanation
The principle of least privilege is the most important principle to apply when granting access to an ERP system that contains a significant amount of personal data. The principle of least privilege states that users should only have the minimum level of access and permissions necessary to perform their legitimate tasks and functions, and no more. Applying the principle of least privilege helps to protect the privacy and security of the personal data in the ERP system, as it reduces the risk of unauthorized or inappropriate access, disclosure, modification, or deletion of the data. It also helps to comply with the privacy laws and regulations, such as the GDPR, that require data controllers and processors to implement appropriate technical and organizational measures to ensure a level of security appropriate to the risk.
References: CDPSE Review Manual, 2021, p. 132