Role-Based Access Provisioning Using Attribute Correlation
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Solution Overview
Problem
New employees face delays and inefficiencies in obtaining access permissions to computing resources due to the need for multiple requests and the potential oversight of necessary permissions, leading to underutilized resources and delayed project completion.
Innovation Solution
A system that uses historical data to correlate attributes and roles, generating data structures to automate the provisioning of access permissions, utilizing neural networks to identify correlations and update policies, and providing access tokens based on these correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple access permission requests are submitted for each computing resource, then access permissions can be obtained, but the process leads to delays and inefficient resource utilization
Solution Approach 1:
The system performs preliminary analysis of historical access permissions data to pre-identify correlations between attributes, roles, and computing resources. When a new employee joins, the system proactively generates recommended access permissions based on these pre-established correlations, eliminating the need for multiple sequential requests and approvals.
Solution Approach 2:
The system enables automated self-service provisioning by using neural networks to autonomously analyze employee attributes and roles, then automatically generating and granting appropriate access tokens without requiring manual submission of multiple access requests or human approval processes.
2Reliability
If multiple access permission requests are required, then specific permissions can be granted, but necessary permissions are frequently overlooked until critically needed
Solution Approach 1:
The system continuously learns from historical access permissions data, using neural networks to refine correlations between employee attributes, roles, and required computing resources. This feedback mechanism ensures that the system progressively improves its ability to identify all necessary permissions, including those that might be overlooked in manual processes.
Solution Approach 2:
The system creates a universal correlation framework that applies across different employee roles, departments, and computing resources. By establishing generalizable relationships between attributes and permissions, the system ensures comprehensive coverage of all necessary access rights regardless of specific job functions or resource types.
3Reliability
If manual access permission requests are processed, then security control is maintained, but computing resources remain underutilized while awaiting approval
Solution Approach 1:
The system implements automated self-service access provisioning where neural networks autonomously evaluate employee attributes and roles against historical data to determine appropriate permissions. Access tokens are granted automatically without manual approval delays, ensuring resources are provisioned immediately when needed while maintaining security through algorithmic enforcement of access policies.
Solution Approach 2:
The system dynamically adjusts access permissions based on changing parameters such as employee role, department, project requirements, and security clearance levels. By continuously monitoring and adapting to parameter changes, the system maintains appropriate security control while ensuring resources are available when organizational needs change.
Data Source
AI summary
Attributes and roles are obtained from a storage device that includes information associated with computing resources. Multiple data structures are generated based, at least in part, on correlations between different types of attributes. In response to a request for access permissions, access tokens are provided based, at least in part, on at least one of the multiple data structures.