When we talk about creating a data warehouse there could be a lot of different data security and regulations which can come into picture. like insurance healthcare etc where there are much higher data security and other regulations because it contains PHI PII kind of data. Below are the kind of measures which can act as a blue print when doing such data warehousing projects.
(A) Data Encryption: Implement encryption to protect sensitive information from unauthorized access.
(B) Access Controls
– Have proper access controls and authentication mechanisms to ensure that only authorized users and processes can access the data warehouse.
– Use role-based access control (RBAC) to manage access privileges.
(C) Auditing and Logging: Enable auditing and logging mechanisms to track and record all activities within the data warehouse, including data access, changes and administrative actions. This is helpful to identify and investigate any issues.
(D) Data Masking and Obfuscation: Use data masking or obfuscation techniques to obscure sensitive data elements, such as personally identifiable information (PII) or financial data
(E) Choice of deployment & Separation of Environments:
– Maintain separate environments for development, testing, staging, and production to prevent accidental exposure or modifications to production data.
– Having the entire setup on the preferred hardware of the client. So it can be on-premise, private cloud or the client’s preferred public cloud.
(F) Compliance with Regulations:
– There are certain sector specific regulations also which needs to be adhered to like GDPR, HIPAA for healthcare etc.
(G) Incident Response & Disaster Recovery:
– Develop and maintain an incident response plan to effectively respond to and mitigate security incidents or data breaches.
– Implement disaster recovery and business continuity plans to ensure data availability and integrity in the event of system failures or disasters.
Tools like Ask On Data, with its simple chat interface powered by AI, can help you simply type and load the data into the data warehouse as well as do the required transformations. It can help in saving around 93% time in creating data pipelines as compared to tradition ETL tools.
If you are looking for some professional guidance you can reach out on www.helicaltech.com