Runtime Workload Monitoring for Adaptive Entity Permissions
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Solution Overview
Problem
Existing systems lack efficient and comprehensive methods for monitoring and managing identity transitions and access permissions within complex compute environments, such as cloud environments, which can lead to security vulnerabilities and compliance issues.
Innovation Solution
A data platform is implemented to monitor and manage identity transitions and access permissions by collecting workload data from agents deployed on compute assets, using polygraphs to analyze behavior patterns, and providing real-time anomaly detection and compliance monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive monitoring and management of identity transitions and access permissions is implemented, then security and compliance are improved, but system complexity increases
Solution Approach 1:
The system segments the monitoring function by deploying separate agents on compute assets that collect workload data independently, then transmit to a central data platform. This divides the complex monitoring task into distributed data collection points and a centralized analysis hub, reducing the complexity burden on any single component while maintaining comprehensive security monitoring.
Solution Approach 2:
The patent introduces a data platform as an intermediary between compute assets and security management systems. This intermediary collects workload data from agents, stores it in data stores, and provides it to anomaly detection systems, thereby simplifying the architecture by creating a dedicated layer for data handling and reducing direct complex interactions between security components.
2Reliability
If real-time anomaly detection is implemented, then security response is improved, but computational resources increase
Solution Approach 1:
The system performs preliminary data collection and storage of workload data in data stores before anomaly detection is needed. By pre-collecting and organizing data from multiple compute assets, the system reduces computational overhead during real-time anomaly detection, as the data is already prepared and indexed for rapid analysis when anomalies occur.
Solution Approach 2:
The patent implements periodic data collection cycles where agents gather workload data at scheduled intervals rather than continuously streaming all data to the analysis system. This periodic approach filters out normal operational noise and reduces computational resources required for real-time processing, while still enabling timely anomaly detection when patterns deviate from baseline behavior.
Data Source
AI summary
An illustrative method includes accessing, by a data platform configured to monitor a compute environment, permissions data representative of a set of permissions that specify how an entity is entitled to interact with resources within the compute environment; collecting, by the data platform, runtime workload data representative of an actual interaction by the entity with the resources over a period of time; and performing, by the data platform based on the runtime workload data, an operation associated with modifying the set of permissions for the entity.