LLM Security Risk Reporting With Critique-Guided Validation
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Solution Overview
Problem
Current cybersecurity tools provide an incomplete and fragmented view of cyber risks, making it difficult for organizations to holistically assess and remediate threats, and there is a lack of standardized methods for quantifying and managing financial risk associated with IT resources.
Innovation Solution
A comprehensive risk quantification and visualization framework using Large Language Models (LLMs) to analyze cybersecurity data from multiple sources, perform Monte Carlo simulations, and generate AI reports on security risk, incorporating zero-trust architectures and cloud-based monitoring to provide a unified view of cyber risks and their financial impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If siloed security tools are used to monitor different security areas, then specific security functions can be performed, but the view of cyber risks becomes incomplete and fragmented
Solution Approach 1:
The patent combines multiple siloed security tools and monitoring functions into a unified risk quantification platform. The system integrates data from various security areas (prevent compromise, lateral movement, data loss, attack surface) into a single holistic view, allowing security leaders to assess overall risk rather than viewing isolated security functions.
Solution Approach 2:
The risk quantification framework serves multiple functions simultaneously: it monitors security posture, quantifies financial risk, generates Monte Carlo simulations, and produces actionable reports. This multi-functional approach replaces the need for separate specialized tools while providing comprehensive risk insights.
2Ease of operation
If manual spreadsheets and third-party tools are used for risk management, then basic risk tracking is possible, but risk quantification and financial impact assessment are insufficient
Solution Approach 1:
The system transforms risk management from qualitative manual tracking to quantitative parameter-based measurement. By using Monte Carlo simulations and financial modeling, the system converts security posture data into precise financial risk estimates, enabling accurate quantification of potential losses and return on security investments.
Solution Approach 2:
The patent replaces manual spreadsheet-based risk tracking with an automated AI-driven quantification system. The system automatically processes security data, performs Monte Carlo simulations, and generates financial risk assessments without manual intervention, improving both precision and efficiency.
3Loss of information
If comprehensive security monitoring is implemented across multiple sources, then holistic risk view is achieved, but system complexity increases
Solution Approach 1:
The system segments the complex monitoring function into distinct modular components: data collection from multiple sources, risk posture assessment, Monte Carlo simulation engine, and report generation. This segmentation allows each component to be optimized independently while working together to provide holistic risk views.
Solution Approach 2:
The patent introduces an AI-driven risk quantification layer that acts as an intermediary between raw security data and business decision-making. This intermediary layer processes and contextualizes data from multiple sources, transforming them into actionable risk insights without requiring direct integration of all data sources.
4Productivity
If AI models are used to generate security reports, then report generation speed and consistency are improved, but verification of accuracy and detection of hallucinations becomes challenging
Solution Approach 1:
The system implements feedback mechanisms where the AI model generates reports that are then verified against security data sources and business context. The feedback loop allows the system to correct hallucinations and ensure accuracy by cross-checking generated insights with actual security posture data and known risk factors.
Data Source
AI summary
Systems and methods for sing Large Language Models (LLMs) to generate an Artificial Intelligence (AI) report on security risk using the cybersecurity data include obtaining cybersecurity monitoring data for an organization where the cybersecurity monitoring data is from a plurality of sources including from cybersecurity monitoring of a plurality of users associated with the organization; inputting the cybersecurity monitoring data to a first Large Language Model (LLM) to generate an initial output for a security report; inputting the initial output to a second LLM for critiquing the initial output against a set of rules to check for predefined flaws and to check for what was done correctly to generate a critique; resolving the initial output and the critique to generate a final output; and providing the final output for the security report.