AI Threat Modeler for Dynamic Remediation Code Generation
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Solution Overview
Problem
Current cyber security systems fail to detect and mitigate unforeseen cyber security threats in real-time, as they lack remediation packages for previously unknown attack strategies.
Innovation Solution
A system that utilizes a server computing device with a processor and memory to analyze application log data, execute a trained artificial intelligence threat modeler, and generate remediation software packages to counteract identified threats, either by executing existing remediation actions or creating new ones based on threat parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current cyber security systems use predefined detection rules and existing remediation packages, then they can detect and mitigate known threats, but they fail to detect and mitigate unforeseen or previously unknown attack strategies
Solution Approach 1:
The system dynamically adapts its detection and response capabilities by using machine learning models that continuously learn from new threat data. The remediation package is generated dynamically based on the specific characteristics of the detected threat, allowing the system to respond to unforeseen attacks while maintaining reliability through automated analysis and decision-making processes.
Solution Approach 2:
The system performs self-service by automatically generating remediation packages without human intervention. When a threat is detected, the system autonomously analyzes the threat characteristics, selects appropriate response actions, and generates the necessary remediation code, enabling rapid response to both known and unknown threats while maintaining consistent detection accuracy.
2Adaptability or versatility
If the system generates new remediation software packages for unknown threats in real-time, then it can mitigate previously unknown cyber security attacks, but the complexity of the system increases
Solution Approach 1:
The system segments the remediation generation process into distinct modular components: threat analysis module, remediation selection module, code generation module, and deployment module. Each component handles a specific aspect of the response process, making the overall complex system manageable through clear separation of concerns and independent optimization of each segment.
Solution Approach 2:
The system introduces an intermediary artificial intelligence layer that mediates between threat detection and remediation execution. This AI intermediary analyzes threats, determines appropriate responses, and generates remediation code, acting as a intelligent broker that simplifies the interaction between detection systems and remediation mechanisms while handling the complexity of real-time decision-making.
3Loss of time
If the system analyzes application log data and executes threat modelers in real-time, then it can identify actual cyber security threats promptly, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing only the most critical and suspicious log entries rather than processing every single log event. The threat modeler selectively applies deep analysis to entries that exhibit threat indicators, while routine entries receive minimal processing, reducing overall computational burden while maintaining rapid threat identification capability.
Solution Approach 2:
The system maintains continuous monitoring and analysis of application log data, with threat models constantly evaluating incoming events. This continuous operation allows the system to build contextual understanding over time and maintain readiness for threat detection without periodic interruptions, optimizing the balance between response time and resource usage through sustained analytical processing.
Data Source
AI summary
Methods and apparatuses are described for automated intelligent detection and mitigation of cyber security threats. A server receives application log data from application servers and analyzes the log data to identify indicia of potential cyber security threats. The server executes a trained threat modeler against the log data and the indicia of potential cyber security threats to identify indicia of actual cyber security threats. The server determines whether a remediation action exists for the identified actual cyber security threats. If a remediation action exists: the server executes the remediation action at the application servers to resolve the actual cyber security threat. If a remediation action does not exist: the server generates remediation parameters based upon the indicia of the actual cyber security threat, generates source code for a software package based upon the remediation parameters, and executes the software package at the application servers to resolve the cyber security threat.