I want to learn about established standards or frameworks for building secure data handling systems, especially in environments with multiple teams, different types of data, and independent data processing pipelines.

Consider this scenario:

• Different teams manage a large dataset, and the data is processed using processing pipelines that function independently.
• Access control is crucial: team members have different levels of access to data and processing pipelines based on their roles and responsibilities.
• Processing pipelines used on data may come from developers from other teams, which means there should be mechanisms to ensure these pipelines do not pose security risks to those using them.
• Clear rules must be in place for granting data access, with mechanisms to track access and actively check whether individuals still require access.
• All data must be continuously monitored for status changes, such as being reclassified from non-confidential to sensitive or changes in terms of use. Data should also be vetted carefully before integration. This requires tools to trace data propagation, perform cleanup when needed, and monitor data status changes over time.

I’m looking for:

• Established frameworks or best practices for organizing secure data systems, including features that inherently prevent insecure processing pipelines and support backtracking and regular checks.
• Recommendations for handling shared processing pipelines, preventing data leaks, and managing sensitive data flow.
• Resources to learn more about building secure, scalable data processing systems in environments with multiple stakeholders.

Are there specific frameworks or tools that are well-suited for addressing these concerns? Any insights or recommendations would be greatly appreciated. I may have missed some important requirements, so I would be thankful if those could be highlighted too.