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Answer: Develop a dynamic view that leverages SQL functions and role-based access control (RBAC) to automatically filter data based on the user's role and region, with built-in compliance checks for regulations like GDPR.
Option C is the best solution because it dynamically controls access to data based on user roles and regions, ensuring compliance with GDPR and other regulations without the need for manual updates. This approach is both scalable and cost-effective, as it automatically adjusts to new roles and regions, and leverages SQL functions for efficient data filtering. It provides a flexible and secure method for managing data access across a multinational corporation.
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In a scenario where you are tasked with implementing a secure data access solution for a multinational corporation, you need to ensure that access to sensitive rows and columns in a dataset is strictly controlled based on user roles across different regions. The solution must comply with GDPR for European users, be cost-effective, and easily scalable to accommodate future roles and regions. Which of the following techniques would be the BEST to achieve this? (Choose one)
A
Implement a single static view that includes all data and use application-level filtering to restrict access based on user roles, ensuring compliance by manually updating the view for GDPR requirements.
B
Create multiple static views for each combination of user role and region, pre-filtering data to meet access control and compliance requirements, which requires manual updates for any changes in roles or regulations.
C
Develop a dynamic view that leverages SQL functions and role-based access control (RBAC) to automatically filter data based on the user's role and region, with built-in compliance checks for regulations like GDPR.
D
Use a dynamic view that includes all data and apply row-level security (RLS) policies to filter data at query time, but this requires additional manual setup for each new region or regulation.
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