Security Drift Detection via Vector Space Analysis
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Solution Overview
Problem
Current methods for verifying the security of cloud resources through text-based comparisons fail to account for contextual differences, leading to potential functional discrepancies being overlooked, and require computationally complex and often manual reviews.
Innovation Solution
A security audit engine encodes security configuration datasets and golden configuration text sentences as vectors in an n-dimensional space, using unsupervised clustering and transport techniques to generate a security drift score, which represents divergence from the golden configuration, enabling automated identification and action on security drift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based comparison techniques are used to verify security settings, then the process is simple to implement, but the measurement precision is insufficient because contextual differences are not accounted for
Solution Approach 1:
The patent transforms security configuration parameters from raw text strings into vector representations in a multi-dimensional space. This parameter transformation enables semantic understanding of security settings, allowing the system to distinguish between functionally equivalent configurations and truly divergent ones, thereby improving measurement precision without requiring excessive complexity
Solution Approach 2:
The patent introduces probability distributions and distance matrices as intermediary representations between the raw security configuration text and the final drift assessment. These intermediaries enable sophisticated comparison by capturing the semantic relationships and contextual meanings of security parameters, resolving the contradiction between accuracy and complexity
2Measurement precision
If complex text-based comparison techniques are used, then the measurement precision improves, but the ease of operation deteriorates due to requiring manual review
Solution Approach 1:
The patent implements self-service through automated drift detection and classification. The system automatically computes distance matrices, compares security configurations, identifies drift instances, and even generates remediation recommendations without requiring manual intervention. This automation maintains high measurement precision while eliminating the need for manual review
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors security configurations, compares them against golden configurations, and automatically adjusts or flags drift instances. This closed-loop feedback system enables precise automated handling of security drift, resolving the contradiction between accuracy and operational ease
3Reliability
If traditional text comparison is used, then the device complexity remains low, but the reliability is insufficient because functional differences are overlooked
Solution Approach 1:
The patent moves the comparison from one-dimensional string matching to multi-dimensional vector space analysis. By representing security configurations as vectors and computing distance matrices in this elevated dimensional space, the system captures nuanced functional differences that traditional text comparison misses, thereby improving reliability while managing complexity through mathematical formalization
Data Source
AI summary
Security drift can be automatically handled in cloud environments. A security audit engine can be configured to extract security configuration datasets from cloud resources and create text sentences from the datasets as well as from a golden configuration. These text sentences can be encoded as vectors in an n-dimensional space. Probability distributions can then be generated using the vectors such as by using an unsupervised clustering algorithm. Distance matrixes can then be generated from the probability distributions. A probability distribution pertaining to a dataset and a probability distribution pertaining to the golden configuration can then be compared and normalized using a transport to thereby yield a security drift score representing a divergence of the corresponding security settings from the golden configuration. When a security drift score exceeds a threshold, the security audit engine can take appropriate action.