Conditional Data Access Rules for Computing Environments
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Solution Overview
Problem
Large organizations face challenges in efficiently managing data access and security for sensitive information due to the complexity of determining appropriate access levels for various data consumers and processing operations, which can lead to inefficiencies and potential misuse of personal data.
Innovation Solution
A data control system that evaluates and configures data access rules based on negotiated conditions and actions between data producers and consumers, automatically generating and applying rule sets to ensure secure and efficient data access while reducing redundant or conflicting actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If granular data access control is implemented to protect sensitive information, then data security is improved, but device complexity increases due to multiple variables involved in access determination
Solution Approach 1:
A data control system is introduced as an intermediary component between data producers and data consumers. This system automatically evaluates access requests against pre-defined data access rules, eliminating the need for complex manual access determination while maintaining granular security control. The intermediary handles the complexity of multi-variable access evaluation centrally.
Solution Approach 2:
The data control system enables data producers and consumers to negotiate and manage their own access rights through automated rule evaluation. The system self-manages access determination by automatically matching requests against rules without requiring manual intervention, reducing operational complexity while maintaining security.
2Productivity
If automated rule evaluation is implemented to manage data access, then productivity is improved by automating access determination, but device complexity increases due to rule management requirements
Solution Approach 1:
The rule management functionality is segmented and decentralized. Each data producer can define their own access rules independently, and the system automatically evaluates requests against these segmented rules. This segmentation reduces overall system complexity by distributing rule management responsibilities rather than requiring centralized complex management.
Solution Approach 2:
The system manages rule complexity by allowing flexible parameter changes in access rules (such as modifying data types, access levels, or temporal constraints) without requiring system-wide reconfiguration. Rules can be dynamically adjusted by individual data producers, and the automated evaluation system adapts to these changes, maintaining productivity while reducing management burden.
3Reliability
If centralized data access control is implemented to ensure security, then data security is improved, but ease of operation deteriorates as data consumers must navigate complex access determination processes
Solution Approach 1:
The data control system serves as an intermediary that transparently handles access determination. Data consumers simply submit access requests without needing to understand or navigate the complex underlying rules. The intermediary automatically evaluates requests and returns results, maintaining security while preserving ease of operation for end users.
Solution Approach 2:
The system provides self-service functionality where data consumers receive automatic responses to access requests without manual intervention. The automated rule evaluation and decision-making process eliminates the need for users to navigate complex access determination procedures, maintaining both security and operational simplicity.
Data Source
AI summary
Described herein are systems and techniques to facilitate efficient access to a data solution based on computing environment conditions. Rules may be generated for a data solution access that include conditional data and execution data. In response to a request for access to the data solution, the conditional data may be evaluated and corresponding execution data may be logged for conditional data that corresponds to the computing environment state. The logged execution data may be evaluated for redundancies and conflicts and the remaining execution data may be executed to facilitate access to the data solution.