Anomalous Role Assignment Detection in Managed Environments
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Solution Overview
Problem
In managed environments, existing role-based access control systems fail to accurately detect suspicious role assignments, leading to potential malicious activities and increased security risks due to the lack of comprehensive anomaly detection across various perspectives.
Innovation Solution
Implementing a processor-based system that applies multiple anomaly detection models from different perspectives (assignee, assigner, tenant, and cross-tenant) to determine if a role assignment is anomalous, with the ability to output alerts when the assignment exceeds predefined thresholds, thereby reducing false positives and identifying potentially malicious actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional role-based access control systems are used, then role assignment management is simple, but suspicious role assignments cannot be accurately detected
Solution Approach 1:
The system segments the anomaly detection task into multiple independent models, each focusing on a specific perspective (assignee behavior, assigner behavior, tenant context, cross-tenant patterns). This segmentation allows each model to specialize in detecting specific types of anomalies without being overwhelmed by the complexity of analyzing all aspects simultaneously, thereby improving detection accuracy while managing system complexity.
Solution Approach 2:
The framework implements a universal multi-model detection system that can analyze role assignments from multiple perspectives using a unified architecture. Each anomaly detection model serves multiple functions by evaluating different aspects of the same role assignment event, allowing the system to comprehensively assess suspicious activities without requiring separate specialized systems for each detection angle.
2Measurement precision
If comprehensive anomaly detection from multiple perspectives is implemented, then detection accuracy improves, but processing resources increase
Solution Approach 1:
The system applies partial action by selectively activating and applying multiple anomaly detection models only when role assignment events occur, rather than continuously monitoring all activities. This approach allows comprehensive multi-perspective analysis precisely when needed (improving detection accuracy) while avoiding unnecessary processing during normal operations (reducing resource consumption).
Solution Approach 2:
The framework dynamically adjusts detection parameters and model application based on the specific characteristics of each role assignment event. By changing which models are activated and how they are applied based on event context, the system optimizes the balance between detection thoroughness and resource utilization, applying comprehensive analysis only when suspicious patterns are detected.
Data Source
AI summary
According to examples, an apparatus may include a processor and a memory on which is stored machine-readable instructions that when executed by the processor, may cause the processor to determine that an entity was granted an anomalous role assignment to a managed environment. The processor may also, based on the determination that the role assignment of the entity is anomalous, identify at least one indicator associated with the role assignment, determine an indicator value corresponding to the identified at least one indicator, and determine whether the indicator value exceeds a predefined threshold value. The processor may, based on a determination that the indicator value exceeds the predefined threshold value, output an alert indicating that the role assignment is suspicious.