In my opinion, "Implementing role-based access control (RBAC) to ensure users have the right permissions" is the most appropriate. In addition, with the Data Mart part combined with GraphQL will also help me limit access better.

All of these options makes sense from user experience. You can use dynamic data masking, network restriction, data catalogue etc. to further improve user experience and data security

Having a well maintained and upto date catalog of enterprise data assets along with RBAC roles and one click option to raise access request to what they need will help strike the balance. Many a times users have to go on a treasure hunt to figure out how to get access to the data and where it is located. A well maintained catalog will bridge the gap between security and user experience.

In my view, utilizing role-based access control (RBAC) alongside dynamic data masking ensures a strong balance between security and usability. Additionally, implementing real-time anomaly detection powered by AI enhances monitoring without disrupting user workflows. To further maintain a seamless experience, I would suggest gradual security updates to assess impacts on usability, alongside encouraging a feedback-based approach to adjust and improve security measures as needed.

1. Implement common RBAC solution across all the components on your data platform i.e Warehouse, reporting platform and ML platforms, so its give consistency to user on data access. 2. Identify User tier and apply dynamic masking based on what they should access based on their role 3. Implement robust Data leakage detection framework so that as long as users stay inside the framework they should not feel any hinderance to access and share data within departments/org

Put in place environmental security that responds instantly to user actions and strange events, making it easier for people to do what they're supposed to. You can make sure that private data stays safe by using granular data masking to give role-based data visibility. Anomaly detection powered by AI should be used for constant monitoring so that quick responses can be made without interrupting normal user flows. Gradually release security updates to see how they affect users and make changes in real time. Lastly, encourage a feedback-driven method where user feedback is used to improve and change security measures. This will help keep trust and productivity high.

In my opinion, the most efficient way to balance user experience with security is by using role-based access control (RBAC) and multi-factor authentication (MFA). These ensure people only access what they need, and their accounts stay secure. Regularly updating security measures and auditing the system without interrupting workflows is also key. To make things smoother, training users on security practices helps them avoid mistakes while staying productive. It’s really about keeping security strong while making sure the system stays easy to use.

One of things that achieve such balance is using data masking before loading data into analytical area. Also, you can consider apply retention policy that flush the data from sandbox after advanced analytic or training models get completed.

Architectural Considerations Design a robust, flexible data warehouse architecture: Microservices-based security models Containerization for isolated security zones Scalable authentication services Cloud-native security frameworks