Cloud Asset Monitoring via Risk-Based Priority Scoring
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Solution Overview
Problem
Conventional monitoring techniques for cloud computing environments are inefficient due to the need for agent-based data collection, which requires permissions and consumes excessive computing resources, and snapshot-based methods generate unnecessary data volumes.
Innovation Solution
A method for prioritizing asset collection by determining priority scores using both internal and external feature values, reducing data collection to only high-priority assets, thereby optimizing resource usage and improving security assessment efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agent-based data collection is used, then data can be collected from assets, but computing resources are excessively consumed and permissions are required
Solution Approach 1:
The patent extracts only the essential monitoring functions from the asset itself by collecting lightweight metadata and configuration information, rather than running full monitoring agents on each asset. This extraction approach collects necessary data while minimizing resource consumption on the monitored assets.
Solution Approach 2:
The patent introduces an intermediary layer that collects data from assets through lightweight mechanisms and processes it centrally. This intermediary approach eliminates the need for heavy agent-based collection while maintaining data gathering capabilities through optimized metadata collection and centralized processing.
2Ease of operation
If snapshot-based methods are used, then data collection is simplified, but unnecessary data volumes are generated
Solution Approach 1:
The patent extracts only the critical security-relevant metadata from asset snapshots, such as configuration information, software inventories, and vulnerability data. This selective extraction maintains the simplicity of snapshot-based collection while dramatically reducing the volume of data that needs to be processed and stored.
Solution Approach 2:
The patent applies different data collection strategies to different assets based on their security criticality and risk profiles. High-value assets receive more detailed monitoring while lower-risk assets use lighter-weight collection methods, optimizing the balance between operational simplicity and data volume management.
3Reliability
If all assets are monitored equally, then comprehensive security coverage is achieved, but resource usage is inefficient
Solution Approach 1:
The patent implements risk-based prioritization that assigns different monitoring intensities to different assets based on their security criticality, vulnerability profiles, and threat exposure. This local quality approach ensures comprehensive security coverage while concentrating resources on high-risk assets, thereby improving overall assessment velocity without compromising security reliability.
Solution Approach 2:
The patent applies partial monitoring action to lower-risk assets and excessive (intensive) monitoring to high-risk assets. This differentiated approach allows the system to maintain comprehensive security coverage while optimizing resource allocation to achieve faster assessment velocities for critical assets.
Data Source
AI summary
Techniques for monitoring assets in a cloud computing environment, comprising: collecting datasets for respective assets in the cloud computing environment, each of the datasets comprising at least some data stored by a respective one of the assets at one or multiple timepoints, the datasets including a first dataset for a first asset of the assets; determining priority scores for the assets using: feature values determined using data in the datasets, and feature values determined using data about the assets and stored in the cloud computing environment, wherein the determining comprises: determining, using data in the first dataset that was stored by the first asset at one or more timepoints, at least one first feature value for the first asset; determining, using data about the first asset and stored in the cloud computing environment, at least one second feature value for the first asset; and determining a priority score for the first asset using the at least one first feature value and the at least one second feature value; and collecting further data about at least some of the assets using the determined priority scores.