Dynamic Authorization Control System for Cloud Security
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Solution Overview
Problem
Current Role-Based Access Control (RBAC) systems are limited by being two-dimensional, static, and unable to adapt to changing environments, leading to security exposures and compliance challenges, especially in cloud computing environments where authentication and access control semantics differ across providers.
Innovation Solution
A dynamic authorization control system that uses machine learning to monitor conditions and events, such as security alerts and business state changes, to automatically and dynamically modify user authorization controls, providing multi-dimensional and transient roles that adapt over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static role assignments are used in traditional RBAC systems, then ease of operation is improved through simple role-based access control, but security reliability deteriorates due to stale permissions that cannot adapt to changing business needs and security threats
Solution Approach 1:
The patent transforms static RBAC into a dynamic system by continuously monitoring multiple data sources (project management systems, HR systems, security event systems) and automatically adjusting user permissions based on current business conditions, security events, and organizational changes. This ensures security reliability is maintained without requiring complex manual intervention.
Solution Approach 2:
The system implements continuous feedback loops by monitoring security events, business project status, and organizational changes, then automatically adjusting permissions based on this feedback. This closed-loop approach maintains security reliability while keeping the system manageable through automated decision-making.
2Adaptability or versatility
If manual role assignment changes are performed by administrators, then adaptability to changing business needs is improved, but productivity deteriorates due to time-consuming manual updates and difficulty in identifying stale permissions
Solution Approach 1:
The system performs self-service by automatically detecting when permissions become stale through continuous monitoring of business projects, organizational changes, and security events. It then automatically adjusts permissions without requiring administrator intervention, significantly improving productivity while maintaining adaptability to changing conditions.
Solution Approach 2:
The system takes preliminary action by proactively identifying potential security risks before they materialize. By continuously monitoring business conditions and security events, it preemptively adjusts permissions to prevent security exposures, improving both adaptability and productivity.
3Adaptability or versatility
If traditional two-dimensional RBAC is used, then ease of operation is maintained through simple role definitions, but adaptability deteriorates due to inability to account for multiple dimensions such as projects, skills, and security contexts
Solution Approach 1:
The patent extends traditional two-dimensional RBAC by adding multiple new dimensions including project context, skill assessments, security event levels, and organizational hierarchy. This multi-dimensional approach dramatically improves adaptability while the automated monitoring and decision-making processes prevent the system from becoming unmanageably complex.
Data Source
AI summary
A system includes at least one processor to receive training data and generate at least one machine learning rule based on the training data to apply when a condition occurs, continually monitor at least one resource associated with a computing network for the condition in the computing network that may trigger an authorization control modification, the condition comprising one of an active project that uses the at least one resource, a security alert level change, a resource locality change, metadata associated with the condition, a skill assessment, and a business state analysis, determine that the condition has occurred in the computing network, and dynamically and automatically modify a user authorization control for at least one particular user responsive to the machine learning rule.