Generative AI Solution Evaluation for Data Compromise Risk
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Solution Overview
Problem
There is a need for a method and system to objectively evaluate generative AI solutions, considering both the value and data security risks associated with their use, as existing systems are complex and opaque, potentially exposing confidential information.
Innovation Solution
A network-based system and method that evaluates generative AI solutions by determining an output score based on user inputs, identifying confidential data, and generating recommendations to mitigate risks, using a computer system with processors, memory units, and communication components to objectively quantify the value and risks of deploying AI solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI tools are used to process confidential data, then productivity and task completion speed are improved, but data security risks and likelihood of data compromise increase
Solution Approach 1:
The system performs preliminary identification and classification of confidential information in input data before it is processed by the generative AI model. By pre-marking sensitive data elements, the system enables the AI to handle productivity tasks while maintaining security awareness, thus resolving the contradiction between speed and security.
Solution Approach 2:
The patent introduces an intermediary layer between the confidential data and the generative AI model. This intermediary system evaluates and classifies data sensitivity, then appropriately routes or masks information, allowing the AI to process data efficiently while the intermediary maintains security controls, thus balancing productivity and data protection.
2Reliability
If comprehensive data security evaluation is performed on generative AI solutions, then data security risks are reduced, but system complexity and evaluation time increase
Solution Approach 1:
The evaluation system is segmented into distinct functional modules: confidential information identification, classification, risk assessment, and recommendation generation. Each module handles a specific aspect of security evaluation independently, making the overall complex system manageable and maintainable while ensuring comprehensive security assessment.
Solution Approach 2:
The system evaluates security by changing and analyzing multiple parameters of the generative AI solution, including data types, model architecture, access controls, and deployment environment. By systematically varying these parameters and assessing their security implications, the system achieves thorough evaluation without requiring complete system redesign.
3Reliability
If detailed classification of confidential information is performed, then data security is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs partial classification by focusing on identifying and marking only the most critical confidential information elements rather than analyzing every piece of data in exhaustive detail. This selective approach maintains high security for sensitive data while minimizing the time overhead, thus resolving the contradiction between security and processing speed.
Data Source
AI summary
A computing device including at least one memory and at least one processor in communication with the at least one memory is disclosed. The at least one processor is programmed to: (i) prompt a user to input a plurality of components of a proposed generative artificial intelligence (GEN AI) solution by causing to be displayed on a user computing device a template requesting the plurality of components; (ii) in response to receiving the plurality of components, evaluate the proposed GEN AI solution by outputting a use score, wherein the use score represents an overall value of deploying the GEN AI solution including a likelihood of a data compromising event occurring as a result of the deployment; and (iii) output a priority report including a comparison of the use score for the current proposed GEN AI solution to other GEN AI solutions being considered.