ML-Based User Access Control for Need-Based Permissions
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Solution Overview
Problem
Existing user access control mechanisms in computing systems are static and prone to excessive permissions, posing security risks due to reliance on predetermined user roles and privileges.
Innovation Solution
Implementing a machine learning model to dynamically determine user access to computing resources based on user profiles and policy documents, allowing for context-aware and need-based access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static user access control mechanisms are used, then device complexity is reduced, but security reliability deteriorates due to excessive permissions and inability to adapt to changing needs
Solution Approach 1:
The access control system transitions from static role-based permissions to dynamic machine learning-based decisions. The system continuously evaluates user profiles, resource characteristics, and contextual factors to determine access rights in real-time, enabling adaptability to changing security requirements without manual reconfiguration.
Solution Approach 2:
The machine learning model autonomously makes access control decisions by analyzing user profiles and policy documents without requiring manual administrative intervention. The system self-adjusts access permissions based on learned patterns and policy constraints, reducing the need for manual security management while maintaining high security standards.
2Reliability
If machine learning model is implemented for dynamic access control, then security reliability is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The machine learning model serves as an intermediary layer between users and computing resources. It receives access requests, evaluates them against learned policies and user profiles, and makes authorization decisions. This intermediary approach enables sophisticated security analysis without requiring complex changes to the underlying resource management infrastructure.
Solution Approach 2:
The system changes the parameters of access control from fixed role assignments to dynamic probability-based decisions. The machine learning model outputs access probabilities or risk scores that can be adjusted based on policy thresholds, enabling flexible security control without hardcoding complex decision logic into the system architecture.
3Adaptability or versatility
If predetermined user roles and privileges are used, then ease of operation is improved, but adaptability deteriorates as users cannot access resources on a need-by-need basis
Solution Approach 1:
User profiles are pre-populated with historical access patterns, preferences, and contextual information before access requests are made. The machine learning model uses this pre-collected data to rapidly evaluate access requests in real-time, combining the flexibility of dynamic decisions with the speed of pre-computed user characteristics.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for dynamically controlling user access to computing resources on a need-by-need basis. An example method can include receiving an access request from a user. The access request may specify one or more computing resources to be accessed by the user. The example method further includes retrieving a user profile associated with the user, identifying a policy document specifying one or more user rights policies for the one or more computing resources, and determining, using a machine learning model, whether to grant or deny the access request based on the user profile and the policy document.