Learning Cognitive Access Control Service for Dynamic Adaptation
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Solution Overview
Problem
Current access control systems require significant administrative effort and intervention, as they lack the ability to dynamically adapt to changing user behaviors and system contexts, leading to inefficiencies in granting or denying access to protected resources.
Innovation Solution
A learning cognitive access control service that utilizes machine learning models to analyze behavioral patterns, usage patterns, and system context to automatically grant or deny access requests, minimizing administrative intervention by dynamically modifying access control mechanisms and configuring them based on monitored data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preconfigured rule sets are used for access control, then access decisions can be made systematically, but significant administrative effort and intervention are required
Solution Approach 1:
The access control system automatically monitors system state, identifies behavioral patterns, and modifies access control mechanisms without requiring continuous administrative intervention. The system serves itself by using machine learning models to make autonomous access decisions based on observed patterns and current context.
Solution Approach 2:
The system continuously monitors access requests, system state, and user behaviors, then uses this feedback to update machine learning models and dynamically adjust access control rules. This closed-loop feedback mechanism enables the system to learn and adapt automatically, reducing the need for manual reconfiguration.
2Reliability
If preconfigured access control rules are used, then access security can be maintained, but the system cannot dynamically adapt to changing user behaviors and system contexts
Solution Approach 1:
The access control system transitions from static preconfigured rules to dynamic rules that automatically adapt to changing conditions. Machine learning models continuously learn from observed user behaviors and system states, enabling the access control mechanisms to evolve and respond to new patterns without losing security effectiveness.
Solution Approach 2:
The system dynamically changes access control parameters such as permitted users, access levels, and time windows based on learned behavioral patterns and current system context. These parameter changes are automatically adjusted as the machine learning models identify new patterns, allowing the system to adapt its security posture dynamically.
3Measurement precision
If manual access control configuration is used, then detailed knowledge of system and users can be applied, but inefficiencies occur in granting or denying access requests
Solution Approach 1:
The manual mechanical process of administrative review and decision-making is replaced with automated machine learning-based analysis. The system uses computational models to analyze behavioral patterns and system context, substituting human administrative processes with automated intelligent systems that operate at much higher speeds.
Solution Approach 2:
The system performs preliminary analysis of access requests by continuously monitoring and learning user behaviors and system contexts before actual access decisions are needed. This advance preparation of knowledge and pattern recognition enables rapid automated decision-making when access requests occur, eliminating delays associated with manual review.
Data Source
AI summary
An indication associated with a request to access a protected object by a subject is received. Using one or more processors, application level behavioral patterns of the subject, context of the request by the subject, usage patterns associated with the protected object, and a current system state are automatically analyzed using one or more machine learning models to determine an analysis result associated with whether to grant the subject access to the protected object. An access control mechanism for the protected object is automatically modified based on the analysis result.