Cloud Resource Monitoring With Event-Driven Security Harvesting
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Solution Overview
Problem
Conventional monitoring techniques for cloud computing environments are inefficient in detecting security risks in real-time, as they rely on scheduled data collection, which can allow malicious activities to go unnoticed and expose the environment to potential threats due to the time and computational resources required for analyzing large volumes of data.
Innovation Solution
An event-driven approach is implemented to harvest data from cloud computing environments in response to specific events, allowing for real-time or near real-time analysis of security risks by obtaining and analyzing data related to cloud computing events, and performing security actions as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scheduled data collection is used for monitoring cloud resources, then comprehensive security analysis can be performed, but the time delay allows malicious activities to go unnoticed and increases computational resource requirements
Solution Approach 1:
The system transitions from static scheduled monitoring to dynamic event-driven monitoring. The monitoring frequency and data collection are dynamically adjusted based on event occurrence - collecting data immediately when security events are detected, while maintaining scheduled collection during normal operations. This resolves the contradiction by making the system responsive to actual security conditions rather than operating on fixed schedules.
Solution Approach 2:
The system establishes event subscriptions and monitoring configurations in advance before security incidents occur. By pre-configuring event sources, filters, and response mechanisms, the system can immediately begin analyzing security events when they happen, eliminating detection delays while maintaining comprehensive monitoring coverage.
2Reliability
If scheduled data collection is used for monitoring cloud resources, then comprehensive security analysis can be performed, but large volumes of data require significant computational resources
Solution Approach 1:
The system extracts and collects only the specific data elements relevant to security events rather than gathering all available data during scheduled monitoring. Event filters and queries are designed to extract only necessary information (e.g., specific event types, affected resources, event metadata), significantly reducing data volume and computational processing requirements while maintaining security analysis completeness.
Solution Approach 2:
The system applies different monitoring intensities and data collection strategies to different cloud resources based on their security relevance and event activity. High-priority resources experiencing security events receive intensive immediate analysis, while low-priority resources undergo lighter scheduled monitoring. This localized approach reduces overall computational resource consumption while maintaining comprehensive security coverage.
3Speed
If event-driven monitoring is implemented, then real-time security analysis is achieved, but system complexity increases due to event subscription management
Solution Approach 1:
The system implements a universal event subscription management framework that handles multiple event sources, types, and priorities through a single standardized interface. The event subscription mechanism is designed to work across different cloud providers and event formats, providing multi-functional capabilities that simplify management complexity while enabling real-time security response across diverse event sources.
Data Source
AI summary
Techniques for event driven harvesting and analysis of cloud computing resources in a cloud computing environment, comprising: obtaining information about at least one cloud computing event in the cloud computing environment; determining if the at least one event is related to the allocation of storage to a cloud computing resource; in response to determining the at least one event is related to the allocation of storage to a cloud computing resource, requesting data from the cloud computing resource; and analyzing the data for the presence of security risks and vulnerabilities.