Cyber Risk Scoring With Attack Path Simulation and Loss Modeling
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Solution Overview
Problem
Existing cybersecurity systems lack a comprehensive, data-driven approach to quantify cyber and operational risks, evaluate security programs, and predict future vulnerabilities, while also accounting for broader brand and reputational concerns.
Innovation Solution
A system and method for operational and cyber risk assessment using a data-driven approach that evaluates security posture, identifies areas for improvement, and simulates attack paths to determine business outcomes, incorporating machine learning and statistical modeling to quantify risks and benefits of security enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive risk assessment and attack path simulation are implemented, then measurement precision of cyber risks is improved, but device complexity and computational resources required increase
Solution Approach 1:
The system segments the cyber risk assessment process into distinct modular components: attack path simulation module, vulnerability assessment module, control effectiveness evaluation module, and business outcome computation module. Each module handles specific aspects of the assessment independently, improving measurement precision while managing system complexity through functional decomposition.
Solution Approach 2:
The system performs preliminary actions by pre-computing attack paths, vulnerability profiles, and control effectiveness metrics before actual risk assessment scenarios are evaluated. This allows the system to have ready-made data structures and models that speed up the actual assessment process while maintaining high measurement precision.
2Reliability
If detailed attack path simulation and multiple risk scenarios are analyzed, then reliability of risk assessment is improved, but loss of time for computation and analysis increases
Solution Approach 1:
The system applies partial action by focusing computational resources on the most critical attack paths and high-probability risk scenarios rather than exhaustively analyzing all possible scenarios. The simulation prioritizes paths with higher risk scores, achieving reliable assessment results while reducing overall computation time by not spending excessive time on low-probability events.
Solution Approach 2:
The system implements periodic action by updating and re-running attack path simulations at scheduled intervals or when significant changes are detected in the network topology or vulnerability profile. This maintains reliable risk assessment without requiring continuous computation, thereby reducing time loss while preserving assessment reliability.
3Measurement precision
If multiple data sources and comprehensive security control data are collected, then measurement precision of security posture is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system introduces intermediary components including standardized data collection agents, normalization layers, and integration adapters that mediate between diverse data sources and the core analysis engine. These intermediaries automatically collect, standardize, and validate data from multiple sources, improving measurement precision while reducing the difficulty of data collection and integration.
Solution Approach 2:
The system implements universal data collection mechanisms that can handle multiple types of security control data (vulnerability scans, configuration audits, incident logs, threat intelligence) through a unified interface and standardized data model. This multi-functional approach improves measurement precision across different data types while reducing the overall difficulty of collecting and measuring diverse security data.
Data Source
AI summary
A system and method for operational and cyber risk assessment that utilizes a data-driven approach to evaluate the current security posture and identify areas for improvement based on the user's desired target profile. This process involves estimating the costs and benefits associated with various security program enhancements, increased, hiring, and control uplifts. The system and method then quantify these benefits in terms of reduction in tail value at risk, expected losses, cyber insurance premiums, and the amount of risk capital set aside. The system simulates attack paths associated with various risk scenarios and uses a risk scenario model to compute losses associated with each attack path for each risk scenario. The results of the simulation may be used to determine one or more business outcomes associated with the costs and benefits of implementing security enhancements.