Cloud Cyber-Risk Assessment via ML Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The shortage of skilled cybersecurity professionals and the complexity of prioritizing vulnerabilities in cloud infrastructure pose challenges in effectively identifying and mitigating cyber-risks, particularly with traditional models being ineffective in real-time threat detection and response.
Innovation Solution
A system and method utilizing machine learning models to assess cyber-risk and loss in cloud infrastructure by deriving vulnerabilities, generating risk indices, and enabling automated actions to mitigate risks through API-based software upgrades and misconfiguration fixes, adopting a Zero-Trust security model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional vulnerability management approaches are used, then security engineers can identify and fix vulnerabilities, but the shortage of skilled professionals and high burnout rates make this unsustainable
Solution Approach 1:
The system enables self-service by using machine learning models to automatically prioritize vulnerabilities and generate fix recommendations without requiring constant human intervention. The ML models process vulnerability data, assess risk levels, and produce actionable insights autonomously, reducing the burden on security engineers while maintaining effective vulnerability mitigation.
Solution Approach 2:
The patent replaces the mechanical system of manual vulnerability assessment with an automated ML-based system. Instead of relying on security engineers to manually analyze and prioritize vulnerabilities, the system uses machine learning models to process vulnerability data, assess risks, and generate prioritization rankings automatically, thereby scaling the capability beyond human limits.
2Measurement precision
If security engineers manually prioritize vulnerabilities, then they can focus on critical issues, but the process is time-consuming and difficult to scale
Solution Approach 1:
The patent replaces manual vulnerability prioritization with an automated machine learning system that processes vulnerability data and generates prioritization rankings. The ML models analyze multiple factors including exploitability, impact, and asset criticality to produce accurate prioritization without the time constraints of manual assessment, enabling both precision and speed.
Solution Approach 2:
The machine learning models serve as intermediaries between raw vulnerability data and actionable prioritization decisions. The ML system processes complex vulnerability information, applies risk assessment logic, and outputs prioritized vulnerability lists, acting as a mediator that translates technical vulnerability data into business-relevant risk rankings without requiring direct human analysis of each vulnerability.
3Loss of information
If comprehensive vulnerability assessment is performed across all assets, then complete risk visibility is achieved, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the vulnerability assessment process into distinct machine learning models that handle different aspects: exploitability assessment, impact analysis, and asset criticality evaluation. This modular approach breaks down the complex comprehensive assessment into manageable segments that can be processed independently and then integrated, reducing system complexity while maintaining complete risk visibility.
Solution Approach 2:
The system manages complexity by dynamically adjusting assessment parameters based on asset criticality and vulnerability severity. The ML models adapt the depth and scope of assessment for different assets, focusing computational resources on high-value targets while maintaining comprehensive coverage where needed, thereby achieving complete risk visibility without uniformly high complexity across all assets.
Data Source
AI summary
The embodiment herein provides a system and a method for assessing a cyber-risk and loss in a cloud infrastructure includes (a) deriving at least one of asset, topology, network or authentication vulnerabilities of a cloud infrastructure, (b) generating a technology risk machine learning model and a technology risk index, (c) generating a compliance risk machine learning model and a compliance risk, (d) generating a ransomware machine learning model and a business risk by processing (i) the compliance risk machine learning model and the compliance risk, (ii) a business input comprising asset information, cash flow, a value of the asset, (e) determining an asset's ransomware risk and loss based on the business risk and (f) automatically enabling one or more actions to mitigate the asset's ransomware risk and loss by fix misconfigurations or upgrading software using an API of cloud infrastructure.