5/6/2023 0 Comments Controlplane rules confidence![]() We’ve come up with a secure deployment architecture for the platform while working with some of our most security-conscious customers, and it’s time that we share it out broadly. ![]() But with Azure Databricks, our customers get to keep all data in their Azure subscription and process it in their own managed private virtual network(s), all while preserving the PaaS nature of the fastest growing Data & AI service on Azure. Solving for data exfiltration can become an unmanageable problem if the PaaS service requires you to store your data with them or it processes the data in the service provider’s network. ![]() The problem assumes even more significance as enterprises start storing and processing sensitive data (PII, PHI or Strategic Confidential) with public cloud services. Since the year 2000, a number of data exfiltration efforts severely damaged the consumer confidence, corporate valuation, and intellectual property of businesses and national security of governments across the world. Data exfiltration is also considered a form of data theft. It is also commonly called data extrusion or data exportation. Given a baseline of those best practices, in this article we walkthrough detailed steps on how to harden your Azure Databricks deployment from a network security perspective in order to prevent data exfiltration.Īs per wikipedia: Data exfiltration occurs when malware and/or a malicious actor carries out an unauthorized data transfer from a computer. ![]() In the previous blog, we discussed how to securely access Azure Data Services from Azure Databricks using Virtual Network Service Endpoints or Private Link. ![]()
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