Cross-System Data Stocktaking for Privacy Integration Misconfigurations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In complex cloud landscapes, ensuring data consistency, deletion at the right time, and resource efficiency are challenging due to diverse technologies, architectures, and varying administrator management, leading to inconsistencies and resource wastage.
Innovation Solution
A data stocktaking system collects inventory information to identify misconfigurations and inconsistencies across the landscape, enabling automatic evaluation and reconfiguration of data privacy integration protocols and replication services to ensure consistent data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is replicated across multiple systems to ensure consistency, then data reliability is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent introduces a data privacy integration component as an intermediary that mediates data replication between systems. This component centralizes the management of replication protocols, object lifecycle events, and consistency checks, thereby maintaining data reliability while reducing the complexity burden on individual systems.
Solution Approach 2:
The patent segments data objects into master data objects and transactional objects with different replication requirements. By categorizing objects and applying targeted replication strategies to each category, the system maintains necessary data consistency while avoiding unnecessary replication overhead, thus reducing overall system complexity.
2Reliability
If data retention periods are extended to ensure compliance, then data availability is improved, but resource consumption increases
Solution Approach 1:
The patent implements dynamic data retention management where retention periods are adjusted based on compliance requirements and data usage patterns. The system automatically extends or reduces retention periods for different data objects, ensuring compliance is maintained while optimizing storage resource utilization by not retaining data longer than necessary.
Solution Approach 2:
The patent changes the parameter of data retention period from a static fixed value to a dynamic value that adapts based on compliance requirements, data type, and system state. This allows the system to meet compliance obligations while minimizing unnecessary resource consumption from prolonged data retention.
3Manufacturing precision
If manual configuration of data privacy protocols is performed, then configuration precision is improved, but operation efficiency deteriorates
Solution Approach 1:
The patent implements self-service automation where the data privacy integration component automatically configures replication protocols, monitors data consistency, and performs reconfiguration based on detected misconfigurations. This eliminates manual configuration operations while maintaining high precision through automated consistency checks and protocol enforcement.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors data replication status, detects misconfigurations, and automatically triggers reconfiguration processes. This closed-loop feedback system maintains configuration precision without requiring manual intervention, thereby improving operation efficiency.
4Measurement precision
If comprehensive data monitoring is implemented to detect inconsistencies, then measurement precision is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal data privacy integration component that performs multiple functions including data replication, consistency monitoring, misconfiguration detection, and automatic reconfiguration. By consolidating these monitoring and management functions into a single multi-functional component, the system achieves comprehensive inconsistency detection without proportionally increasing overall system complexity.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for data privacy. One example method includes receiving, from multiple systems in a multi-system landscape, data stocktaking data regarding objects in respective systems. The data stocktaking data comprises, for each respective system, a list of objects under processing in the respective system and a list of objects not under processing in the respective system. The data stocktaking data is evaluated at a central monitoring system to determine at least one misconfiguration of a data privacy integration component that manages data privacy integration in the multi-system landscape. For each identified misconfiguration, a reconfiguration of the data privacy integration component is identified. The identified reconfiguration of the data privacy integration component is applied to correct the misconfiguration.