AI Cyber Incident Simulation for Autonomous Network Response
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Solution Overview
Problem
Existing cyber threat protection systems are insufficient in addressing modern threats due to reliance on passive, generalized simulations and human red teams, which are costly and fail to identify specific vulnerabilities in organizations, leading to inefficient resource allocation and potential network compromise.
Innovation Solution
An AI-based cyber security system with a simulation module, attack path modeling module, and autonomous response module that simulates cyber attacks on a virtualized network, calculates paths of least resistance, and prioritizes security resources based on historical data and device importance to predict and mitigate cyber incidents autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human red teams are hired to test defense systems, then vulnerability detection capability is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent creates a virtualized copy of the network environment including virtual devices, connections, and data that mirrors the actual network. This virtual copy can be used for security testing without involving expensive human red teams, as automated simulations can test vulnerabilities in the virtual environment and translate findings to the real network.
Solution Approach 2:
The patent replaces the mechanical system of human red team operations with an automated computer-based simulation system. The virtualization platform and automated testing tools substitute for human analysts, reducing cost while maintaining or improving detection capability through systematic, repeatable testing protocols.
2Measurement precision
If traditional vulnerability scanners are used to test network devices, then vulnerability identification is improved, but network stability deteriorates due to actual testing impact
Solution Approach 1:
The patent creates a virtualized copy of the network environment including virtual devices, connections, and data that mirrors the actual network. This virtual copy can be used for security testing without involving expensive human red teams, as automated simulations can test vulnerabilities in the virtual environment and translate findings to the real network.
Solution Approach 2:
The virtualized network environment acts as an intermediary between the vulnerability testing process and the actual production network. Tests are performed on the virtual copy, and results are translated back to identify real vulnerabilities without directly impacting network stability.
3Measurement precision
If passive data gathering methods are used to identify vulnerabilities, then detection coverage is improved, but response time and effectiveness worsen due to inability to proactively identify specific vulnerabilities
Solution Approach 1:
The patent performs vulnerability assessments and attack path simulations in advance before actual cyber threats materialize. By proactively identifying vulnerabilities and modeling potential attack paths in the virtualized environment, the system prepares defense strategies ahead of time, reducing response time when real threats occur.
Solution Approach 2:
The system dynamically adapts the virtualized environment and simulation parameters based on emerging threats and organizational changes. Rather than static passive scanning, the system continuously updates attack models and re-simulates to identify newly exposed vulnerabilities, maintaining high detection coverage while enabling rapid response to changing conditions.
Data Source
AI summary
An apparatus may include a set of modules and artificial intelligence models to detect a cyber incident, a simulator to simulate an actual cyber attack of the cyber incident on a network including physical devices being protected by the set of modules and artificial intelligence models; and a feedback loop between i) the set of modules and artificial intelligence models and ii) the simulator, during an ongoing detected cyber incident. An attack path modeling module is configured to feed details of the detected incident by a cyber threat module into an input module of the simulator, and to run one or more hypothetical simulations of that detected incident in order to predict and control an autonomous response to the detected incident. Any software instructions forming part of the set of modules, the artificial intelligence models, and the simulator are stored in an executable form in memories and executed by processors.