Threat Detection Layer for Blocking Illegitimate AI Queries
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Solution Overview
Problem
Conventional AI-based models are limited to single source architectures, struggle with complex queries, and have increased susceptibility to nefarious attacks as they scale, necessitating a system to detect and block illegitimate queries.
Innovation Solution
Implementing a multi-layer neural network with a supplemental threat detection layer that includes a classifier and threat intel pattern for continuous updates, using combinational reasoning to determine query legitimacy and reject illegitimate queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the size and complexity of AI based models continue to increase to field increasingly complex queries, then the capability to handle complex queries is improved, but the susceptibility to nefarious attacks increases
Solution Approach 1:
The patent divides the AI model into multiple independent layers: initial processing layers and a supplemental threat detection layer. Each layer has a specialized function - the initial layers process complex queries while the threat detection layer independently evaluates them for malicious content. This segmentation allows the model to maintain high capability while adding security without requiring a complete redesign of the entire system.
Solution Approach 2:
The threat detection layer acts as an intermediary between the initial processing layers and the final output. It receives outputs from the initial layers, evaluates them against known illegitimate query patterns, and determines whether to allow or block the query. This intermediary layer provides security without interfering with the core functionality of handling complex queries.
2Adaptability or versatility
If conventional products process more complex prompts for longer amounts of time, then the ability to remain relevant is maintained, but resource consumption increases
Solution Approach 1:
The threat detection layer performs preliminary evaluation of queries before they are fully processed by the main AI model. By checking for illegitimate patterns early in the processing pipeline, the system can quickly identify and block malicious queries without consuming the full computational resources required for complete processing, thus reducing overall resource consumption while maintaining relevance.
Solution Approach 2:
The system applies partial processing to queries by using the threat detection layer to evaluate only the necessary aspects of a query for security purposes. This partial action approach allows the system to remain relevant by detecting threats without performing the excessive full processing that would consume unnecessary resources for obviously malicious or simple queries.
Data Source
AI summary
A computer-implemented method, according to one approach, includes: causing a multi-layer neural network to evaluate a user query received from an endpoint device by processing the user query with one or more initial layers of the neural network. In response to receiving an output from the initial layers at a supplemental threat detection layer of the neural network, the user query processed with the threat detection layer. Processing the user query with the threat detection layer includes using a classifier to compare the user query to activation data outlining known illegitimate queries. Moreover, combinational reasoning is used to determine whether the user query is legitimate based at least in part on: an output of the classifier, and behavioral information received from a threat intel pattern. The user query is further intentionally rejected in response to the threat detection layer determining the user query is not legitimate.