Customer Data Handling Toolkit for Secure Cloud Diagnostics
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Solution Overview
Problem
Cloud service providers face challenges in securely managing and tracking customer data, particularly derivative data, which is often excluded from protection scopes, leading to difficulties in adhering to regulations like GDPR and CCPA, and ensuring data erasure and privacy.
Innovation Solution
The implementation of a Customer Data Handling (CDH) toolkit that provisions ephemeral containers for diagnostic data access, ensuring data remains within a secure environment, with access controls and automated purging mechanisms to maintain data trackability and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If diagnostic data is downloaded to engineer's local workstation for parsing and analysis, then data accessibility and analysis capability are improved, but data trackability and security are lost
Solution Approach 1:
A secure data access environment (containerized workspace) is introduced as an intermediary between the engineer's local workstation and the customer data repository. This mediator allows engineers to access and analyze diagnostic data without downloading it to their local machines, maintaining data trackability and security while providing full analysis capability through embedded parsing tools in the containerized environment.
2Device complexity
If derivative data is excluded from protection scope, then data handling complexity is reduced, but compliance with GDPR and CCPA becomes difficult
Solution Approach 1:
Data is segmented into different types (customer content, derivative data, diagnostic data) with distinct protection levels and handling procedures. Derivative data is segregated and processed through automated pipelines that maintain compliance without requiring manual intervention, while customer content receives full protection. This segmentation reduces overall handling complexity while ensuring regulatory compliance for each data category.
3Productivity
If data is allowed to leave secure environment for analysis, then diagnostic capability is improved, but data security and trackability are compromised
Solution Approach 1:
A containerized secure workspace acts as an intermediary environment that provides full diagnostic capability with parsing and analysis tools while maintaining data security. The containerized environment isolates the analysis process from the engineer's local system, preventing data exfiltration while enabling comprehensive diagnostic work on customer data without it leaving the secure cloud environment.
4Ease of operation
If engineers manually delete data from local workstations after use, then data purging capability is provided, but data loss and compliance reliability occur due to human error
Solution Approach 1:
The system provides self-service automated data purging that occurs without human intervention. After diagnostic tasks are completed, the containerized workspace automatically purges all customer data and derivative data according to retention policies and compliance requirements. This eliminates human error while maintaining operational flexibility, as the system autonomously manages the complete data lifecycle from access to purging.
Data Source
AI summary
Various embodiments of the present technology generally relate to systems and methods for secure customer data handling. More specifically, some embodiments relate to handling of derivative data as a provider in a manner that supports security and provides a stronger level of control over the data. The solution supports four core principles of customer data handling: no export of customer data, unless authorized; remote operations only via shell access or equivalent; temporary and task-based privileges; and diagnostic data to be ephemeral. The customer data handling system herein includes a central repository for the storage of diagnostic data, an upload tool for uploading to the central repository and automated staging on containers, a diagnostic virtual machine that enables task-based access to diagnostic data and analysis tools hosted on a dedicated container, and an application for handling requests, provisioning and staging containers, and purging.