Security Posture Recommender Using Deployment-Aware User Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing recommender systems fail to consider how users deploy and use security products or data descriptive of assets within a computing environment, leading to inadequate security posture enhancements.
Innovation Solution
A security platform that generates recommendation data based on similarities and differences between a user's deployment data and that of similar users, using clustering and collaborative filtering to determine modifications to security components and configurations, thereby improving the security posture of a deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommender systems are used that rely on user selections and history, then user preference prediction is improved, but security posture enhancement is inadequate because deployment data and asset information are not considered
Solution Approach 1:
The patent merges traditional collaborative filtering with deployment data analysis and asset information processing. The hybrid recommender system combines user selection history with technical deployment characteristics and asset vulnerability data to generate comprehensive security recommendations that address both user preferences and actual security needs.
Solution Approach 2:
The patent introduces deployment data and asset information as intermediary elements that bridge user preferences and security recommendations. These intermediaries provide technical context about the computing environment, enabling the system to translate user preferences into actionable security posture enhancements specific to each deployment.
2Device complexity
If security recommendations are generated without considering deployment data and asset information, then system simplicity is maintained, but the recommendations fail to address specific security vulnerabilities and deployment characteristics
Solution Approach 1:
The patent segments the recommendation generation process into multiple components: user preference analysis, deployment data processing, asset information evaluation, and integrated recommendation generation. This segmentation allows the system to handle complex security analysis through modular processing stages, managing complexity while comprehensively addressing security vulnerabilities.
Solution Approach 2:
The patent adds new dimensions to the recommendation system by incorporating deployment data dimensions (security component usage, configuration settings) and asset information dimensions (vulnerability profiles, criticality ratings). This multi-dimensional approach enables targeted security recommendations without overwhelming system complexity through structured data organization.
3Reliability
If security posture enhancement requires comprehensive analysis of deployment data and asset information, then security improvement is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing deployment data and asset information before recommendation generation. Deployment characteristics, asset vulnerabilities, and security component configurations are analyzed and stored in optimized formats in advance, enabling faster recommendation generation when security assessments are needed without compromising analysis comprehensiveness.
Data Source
AI summary
Various embodiments include systems and methods to implement a security posture recommender system. The security posture recommender system may improve the security posture of a deployment of assets by generating recommendation data indicating how to modify the deployment of assets. A deployment may be described by deployment data. The recommendation data may be based on similarities and/or differences between deployment data for a particular user and deployment data associated with users that are within a cluster of users similar to the particular user.