Cloud Service Risk Discovery Using Risk Object Similarity
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Solution Overview
Problem
Cloud-based service providers face significant challenges in conducting risk threat modeling (RTM) due to the time-consuming and knowledge-intensive nature of the process, leading to inefficiencies and potential duplication of efforts.
Innovation Solution
A service risk discovery system that employs machine learning models to analyze risk objects, generate training data, and calculate pairwise similarity between risk observations, enabling rapid and efficient risk identification and recommendation for cloud-based services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual risk threat modeling is conducted for cloud-based services, then risk identification accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary risk analysis by training machine learning models on historical risk data from multiple cloud services before actual risk assessment is needed. This pre-training phase enables the system to quickly identify and recommend risks for new services without requiring time-consuming manual analysis at the time of service deployment.
Solution Approach 2:
The system creates vector representations (copies) of risk observations from historical data, allowing it to compare and match risk patterns across different services. By converting risk descriptions into vector forms, the system can efficiently identify similar risks without re-analyzing each service manually, thus reducing time consumption while maintaining accuracy.
2Adaptability or versatility
If comprehensive risk analysis is performed across multiple cloud services, then risk coverage is improved, but computational complexity and resource requirements increase
Solution Approach 1:
The system transforms risk observations into vector representations, changing the parameter space from text-based risk descriptions to numerical vector forms. This transformation enables efficient computation of similarities and relationships across multiple services, allowing comprehensive risk coverage without proportionally increasing computational complexity.
Solution Approach 2:
The system develops a universal risk analysis model that can be applied across different cloud services and domains. By training on diverse historical risk data from multiple services, the model achieves broad adaptability and can identify risks across various service types without requiring service-specific customization, thus improving coverage while managing complexity.
3Manufacturing precision
If traditional risk threat modeling methods are used, then detailed risk analysis is achieved, but knowledge requirements and operational difficulty increase
Solution Approach 1:
The system performs risk analysis automatically without requiring manual intervention from security experts. The machine learning model independently processes service configurations, compares them against historical risk patterns, and generates risk recommendations autonomously. This self-service capability maintains detailed risk analysis quality while dramatically reducing operational difficulty and knowledge requirements.
Solution Approach 2:
The system replaces manual mechanical risk analysis processes with automated machine learning-based analysis. Instead of requiring security professionals to manually examine service configurations and identify risks, the system uses trained models to automatically perform the analysis, substituting human expertise with algorithmic processing that maintains detail while reducing operational complexity.
4Measurement precision
If risk threat modeling is performed for each service independently, then service-specific accuracy is improved, but overall productivity and efficiency decrease
Solution Approach 1:
The system merges risk analysis across multiple cloud services by training a unified machine learning model on aggregated historical risk data from various services. This combined approach enables the model to learn common risk patterns and apply them across different services, maintaining service-specific accuracy while improving overall productivity through shared knowledge and reduced redundant analysis.
Data Source
AI summary
The described technology provides a service risk discovery system for recommending, for a cloud based service sharing the service group hierarchy, a predetermined number of top risk observations based on the ranking of the one or more of the risk observations. The system generates the ranking by joining a plurality of risk objects of one or more of the group of cloud based services based on a service identification field of the plurality of risk objects to generate a service group risk vector, the service group risk vector including a plurality of risk observations for the group of cloud based services.