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

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.