Cloud Resource Prioritization Using Peak Signals for Security Posture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Organizations face challenges in understanding the data posture and access control of sensitive information stored in cloud environments, making it difficult to identify and mitigate risks from both internal and external malicious actors.
Innovation Solution
A cloud security posture analysis system that scans cloud assets in-place using agent-less scanners, generating metadata to detect peak signals representing risk and vulnerability, and provides a graphical interface for prioritized resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud data is scanned and analyzed to identify security risks, then security risk detection capability is improved, but data exposure risk increases
Solution Approach 1:
The patent introduces an intermediary processing layer that scans and analyzes cloud data without directly exposing the actual data. The system uses metadata generation and peak signal detection to identify security risks while maintaining data isolation, thus resolving the contradiction between detection capability and data exposure risk.
Solution Approach 2:
The system creates copies of data metadata and characteristics rather than handling the actual sensitive data. By analyzing copies and generating metadata representations, the system achieves security risk detection without exposing the original data, thereby improving detection capability while minimizing data exposure risk.
2Loss of information
If comprehensive data analysis is performed on all cloud resources, then security posture understanding is improved, but system complexity increases
Solution Approach 1:
The patent extracts only the essential security-relevant features and metadata from cloud data rather than performing comprehensive analysis of all data. By taking out only the critical security posture information needed for risk assessment, the system improves security understanding while avoiding the complexity of analyzing every data element.
Solution Approach 2:
The system applies different analysis depths and methods to different cloud resources based on their security criticality. High-value resources receive more thorough analysis while lower-risk resources receive streamlined assessment, optimizing security posture understanding without uniformly increasing system complexity across all resources.
3Productivity
If peak signal detection is used to prioritize cloud resources, then risk mitigation efficiency is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary peak signal detection and resource prioritization before full security assessment. By identifying high-value cloud resources through peak signal detection in advance, the system can focus detailed analysis on these prioritized resources, improving overall risk mitigation efficiency while managing measurement precision requirements through staged analysis.
Data Source
AI summary
The technology disclosed relates to analysis of data posture of a cloud environment. In particular, disclosed technology relates to a system and method for analyzing cloud assets, such as storage resources, compute resources, etc. to detect peak signals based on occurrences of sensitive data types or other data classifications in cloud assets. A computing system is configured to access data in plurality of cloud resources and, on a cloud resource-by-cloud resource basis, attribute a plurality of data sensitivity parameters to the data in a given cloud resource of the plurality of cloud resources, and generate a peak value indicating an appraisal of the data in given cloud resource based on the plurality of data sensitivity parameters attributed to the data. A graphical interface includes graphical objects configured to visually represent plurality of cloud resources, plurality of data sensitivity parameters, and the peak values generated for the plurality of cloud resources.