Chat Input Security Layer for Sensitive ML Data Screening
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Solution Overview
Problem
Businesses face risks of intellectual property loss and legal issues when using machine learning models due to accidental data dissemination, lack of transparency, and compliance with data protection regulations, making it difficult to select appropriate models.
Innovation Solution
A system and method for monitoring data input into machine learning models, incorporating a chat interface with security modules to prevent sensitive information dissemination and ensure compliance, using user interfaces with machine learning functionality and secondary security devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to increase efficiency and improve decision-making, then productivity is improved, but intellectual property loss risk increases
Solution Approach 1:
The system performs preliminary scanning and analysis of data inputs before they are transmitted to machine learning models. The security module proactively identifies potential intellectual property violations, sensitive information, or non-compliant data in advance, preventing them from entering the model. This preliminary action allows the system to maintain productivity benefits while blocking harmful data transmission.
Solution Approach 2:
The system introduces a security module as an intermediary component between the data input interface and the machine learning model. This intermediary scans, analyzes, and filters data before it reaches the model, acting as a protective barrier that prevents intellectual property loss and ensures compliance while allowing legitimate data to flow through to maintain productivity.
2Measurement precision
If machine learning models collect data to assist with the learning process, then model accuracy is improved, but data protection compliance becomes difficult
Solution Approach 1:
The system performs preliminary compliance checking and data validation before data is transmitted to the machine learning model. The security module analyzes data for compliance with data protection regulations and personal information protection requirements in advance, ensuring that only compliant data enters the model, thus maintaining both accuracy and compliance.
Solution Approach 2:
The system implements feedback mechanisms where the security module continuously monitors data inputs and provides feedback to block non-compliant data while allowing compliant data to proceed to the model. This feedback loop ensures ongoing compliance maintenance while preserving model training accuracy through legitimate data input.
3Productivity
If third-party machine learning models are used to improve efficiency, then productivity is improved, but legal risks increase
Solution Approach 1:
The system introduces a security module as an intermediary layer between business data and third-party machine learning models. This intermediary scans and filters data to prevent transmission of sensitive, proprietary, or non-compliant information to external models, thereby reducing legal risks associated with data breaches, intellectual property infringement, and regulatory violations while maintaining the productivity benefits of using third-party models.
Solution Approach 2:
The system converts potential harmful data inputs (that could cause legal issues) into beneficial security alerts and blocking actions. By identifying problematic data patterns and transforming them into prevention mechanisms, the system protects the business from legal risks while maintaining efficient use of third-party machine learning models for productivity improvement.
4Productivity
If machine learning models are used in team-based software environments, then collaboration efficiency is improved, but accidental data dissemination risk increases
Solution Approach 1:
The system performs preliminary security scanning of all data inputs before they are transmitted to machine learning models in team-based environments. This preliminary action prevents accidental data dissemination by identifying and blocking sensitive information, trade secrets, or non-compliant data before it can be inadvertently shared with external models, thus maintaining collaboration efficiency while preventing data leaks.
Data Source
AI summary
A system and method for system and method for monitoring data before it is input into a machine learning model is provided. Generally, the system and methods of the present disclosure are designed to allow for the secure use of machine learning modules in virtual team environments. A chat module may be used to allow a user to control the use of one or more machine learning modules by inputting commands. The chat module may be incorporated into an existing user interface to add machine learning module functionality to said existing user interface. In some embodiments, a security module may monitor input data entered into the chat module by a user to prevent sensitive information from being distributed to the machine learning module.