AI Threat Mitigation Routing for Utilization-Balanced Security Response
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Solution Overview
Problem
The complexity of computer attacks is increasing, and existing threat mitigation systems struggle to effectively manage and optimize the utilization of generative AI resources across multiple computing systems and subsystems, leading to inefficiencies and potential vulnerabilities.
Innovation Solution
A threat mitigation system that monitors and routes requests to generative AI resources based on utilization statistics and routing restrictions, optimizing the use of a pool of available generative AI resources including various types and accounts/regions, enhancing the detection and response to security events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pool of generative AI resources is managed across multiple computing systems, then the system's ability to detect and respond to security events improves, but the complexity of managing and optimizing resource utilization increases
Solution Approach 1:
The patent introduces a threat mitigation system as an intermediary layer between multiple computing systems and generative AI resources. This intermediary monitors resource utilization statistics, manages routing decisions, and coordinates security responses across the distributed environment, thereby improving security detection while managing the complexity of resource coordination.
2Adaptability or versatility
If generative AI resources are distributed across multiple accounts and regions, then system versatility and resource availability improve, but the difficulty of monitoring and optimizing utilization increases
Solution Approach 1:
The threat mitigation system implements universal monitoring capabilities that function across multiple accounts and regions simultaneously. The system uses standardized utilization statistics and routing restriction mechanisms that work consistently across different generative AI resources, enabling versatile resource access while maintaining uniform monitoring and optimization practices.
3Productivity
If routing decisions are made based on utilization statistics and routing restrictions, then resource optimization improves, but the computational overhead and processing time increase
Solution Approach 1:
The system establishes routing restrictions and utilization thresholds in advance, before actual resource allocation decisions are needed. By pre-configuring routing rules and monitoring baselines, the system reduces the computational complexity of real-time routing decisions, enabling efficient resource optimization without significant processing delays.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for defining a pool of available generative AI resources, wherein the pool of available generative AI resources includes a plurality of discrete generative AI resources; monitoring the utilization of the plurality of discrete generative AI resources to define utilization statistics; receiving a request for the pool of available generative AI resources; and routing at least a portion of the request to one of the plurality of discrete generative AI resources based, at least in part, upon the utilization statistics.