Layered AI Model Logic for Transparent Security Analysis
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Solution Overview
Problem
AI models operate as 'black boxes,' lacking transparency, which hinders validation of their outputs, security, and adherence to regulations, and complicates diagnosing and addressing security vulnerabilities.
Innovation Solution
Construct a layered AI model with distinct layers tailored to specific contexts, each with defined variables and model logic, generating layer-specific responses that are aggregated to provide transparent decision-making insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models operate as black boxes with opaque algorithms, then the model can provide dynamic and nuanced security analysis, but the transparency and trustworthiness of the model deteriorates
Solution Approach 1:
The patent segments the AI model into multiple interpretable layers, each handling specific aspects of security analysis. This segmentation allows the model to maintain its dynamic analysis capability while making the decision-making process transparent by showing which layer contributed which finding.
Solution Approach 2:
The patent introduces an intermediary explanation layer that translates the black box AI decisions into human-understandable security findings. This intermediary component bridges the gap between the opaque algorithm and the user, providing transparency without sacrificing the model's analytical capabilities.
2Reliability
If AI models use complex opaque algorithms, then the model can identify potential threats and vulnerabilities, but the ability to verify integrity and assess susceptibility to adversarial attacks deteriorates
Solution Approach 1:
The patent divides the complex AI model into distinct interpretable layers, each with a specific function in threat detection. This segmentation maintains the model's high accuracy while reducing the perceived complexity by organizing the algorithm into manageable, explainable components.
Solution Approach 2:
The patent uses visual indicators (such as highlighting affected variables or changing display states) to show which parts of the model are active and how they contribute to the output. This makes the complex algorithm's behavior visible and assessable without simplifying the underlying complexity.
3Productivity
If AI models process vast amounts of data dynamically, then the model provides nuanced security analysis, but the lack of visibility into inner workings hinders security validation
Solution Approach 1:
The patent implements feedback mechanisms that provide information about the model's internal state and decision-making process. This feedback loop allows security analysts to validate the model's integrity by observing how input data flows through the layers and transforms into output findings.
Solution Approach 2:
The patent introduces intermediary components that mediate between the high-productivity black box processing and the need for validation. These intermediaries expose relevant internal states and transformations, making it possible to detect and measure model integrity without reducing the model's data processing capability.
Data Source
AI summary
Systems and methods for constructing a layered artificial intelligence (AI) model are provided. The technology determines a set of layers and a set of variables for each layer for the AI model, with each layer relating to a specific domain context of the AI model. Using the layers, the AI model is trained to create layer-specific model logic for each layer using the variables of the layer. By applying the layer-specific model logic to incoming command sets, the model produces detailed layer-specific responses. The trained AI model then generates overall responses to command sets by aggregating the layer-specific responses, along with weights for each layer.