External Computing Environment Risk Assessment With LLM Mismatch Analysis
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Solution Overview
Problem
Existing methods for assessing security risk from external computing environments are manual, point-in-time, and lack real-time monitoring, leading to inefficiencies and potential security breaches.
Innovation Solution
A system utilizing a large language model (LLM) to analyze risk metrics from external computing environments, identify mismatches, and compute real-time security risk through automated question-answering and data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual security risk assessment methods are used, then implementation simplicity is maintained, but assessment accuracy and real-time monitoring capability deteriorate
Solution Approach 1:
The patent introduces an intermediary system comprising automated data collection mechanisms, risk metric calculation modules, and threshold comparison engines that mediate between raw security data and risk assessment results. This intermediary layer automates the assessment process, improving accuracy while managing complexity through modular design.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with automated computational systems. Machine learning models and algorithmic risk calculations substitute human analysts, enabling real-time, consistent, and accurate security risk assessments without manual intervention.
2Speed
If point-in-time assessment methods are used, then system simplicity is maintained, but real-time monitoring capability and response speed deteriorate
Solution Approach 1:
The patent implements continuous monitoring and assessment mechanisms that operate constantly rather than at discrete intervals. Data collection, risk metric calculation, and threshold comparison occur continuously, enabling real-time detection of security risks and immediate response capabilities.
Solution Approach 2:
The patent introduces dynamic risk thresholds and adaptive assessment parameters that adjust in real-time based on changing security conditions. The system dynamically updates risk metrics and compares them against evolving thresholds, enabling speed and responsiveness while managing complexity through adaptive algorithms.
3Measurement precision
If comprehensive risk metric analysis is performed, then assessment accuracy is improved, but computational complexity and processing time worsen
Solution Approach 1:
The patent divides comprehensive risk metric analysis into segmented, modular components. Different risk metrics (data sensitivity, access control, threat intelligence) are calculated and assessed separately through dedicated modules, then aggregated to form the overall security risk assessment. This segmentation improves accuracy while reducing processing time through parallel computation.
Solution Approach 2:
The patent transforms complex risk metric data into standardized parameters and normalized values that can be processed efficiently. By changing the representation of risk metrics into comparable parameters with defined thresholds, the system achieves accurate analysis without excessive processing time.
Data Source
AI summary
A method, comprising: asking a large language model (LLM) to generate for each value of multiple values of each risk metric of multiple risk metrics, at least one question that is correlated with an answer related to said each value of said each risk metric, wherein each of the risk metrics is indicative of a security risk associated with the external computing environment interfacing with the target computing environment, obtaining questions from the LLM, generated following said asking, obtaining responses to the questions, analyzing mismatches between the responses and the values of the risk metrics, computing weights, each weight is associated with a risk metric for which a mismatch is identified, and computing an assessment of a real time security risk as an aggregation of the weights.