Email Security Screening by Recipient Category and Content Segments
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Solution Overview
Problem
Existing email systems lack effective filtering and screening capabilities, particularly for sensitive information, leading to inadequate risk assessment and increased data exposure due to misclassification and insufficient security measures.
Innovation Solution
A secure computing infrastructure that intercepts electronic messages before transmission, categorizes recipient devices, generates content segments using machine learning, and applies security parameters based on recipient categories to block incompatible messages, utilizing a server-based or hybrid model for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard email security protocols are applied, then transmission security is improved, but computing resource utilization increases and vulnerabilities are introduced
Solution Approach 1:
The system performs preliminary classification of electronic messages into risk categories (low, medium, high) before applying security protocols. This preliminary action allows the system to selectively apply security measures based on risk level, avoiding unnecessary computational overhead for low-risk messages while maintaining strong security for high-risk messages, thus resolving the contradiction between transmission security and computing resource utilization
Solution Approach 2:
Different security protocols and classification mechanisms are applied to different categories of messages based on their risk levels. High-risk messages receive comprehensive security scrutiny, while low-risk messages use streamlined protocols. This localized approach ensures that computing resources are concentrated where most needed, improving overall system efficiency without compromising security where required
2Productivity
If standard email filtering is used, then transmission efficiency is maintained, but filtering and screening capabilities for sensitive information are insufficient
Solution Approach 1:
The system dynamically adjusts the depth and type of content analysis based on the risk category of the message. For low-risk messages, standard efficient filtering is applied to maintain transmission speed. For high-risk messages, the system dynamically switches to more intensive machine learning-based content analysis to improve detection accuracy. This dynamic adaptation allows the system to optimize both transmission efficiency and sensitive information detection accuracy under different conditions
3Ease of operation
If generic security measures are applied to all emails, then implementation simplicity is maintained, but protection adequacy for high-risk emails is insufficient
Solution Approach 1:
The system segments the email population into distinct risk categories (low, medium, high) based on content analysis and recipient information. This segmentation allows the system to apply different security measures to different segments: simple generic measures for low-risk emails maintain ease of operation, while enhanced targeted security measures for high-risk emails improve protection adequacy. The segmentation approach makes the complexity of differentiated security management tractable
4Measurement precision
If pre-transmission interception and analysis is implemented, then risk assessment accuracy is improved, but transmission time increases
Solution Approach 1:
The system performs partial pre-transmission analysis by focusing computational resources on the most critical aspects of risk assessment for each message category. For low-risk messages, only essential checks are performed before transmission. For high-risk messages, more comprehensive analysis is conducted. This partial action approach improves risk assessment accuracy for messages that require it while minimizing the time loss for the majority of low-risk messages
Data Source
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AI summary
Secure computing infrastructure for electronic messages is described herein. A system can intercept an electronic message for transmission to a recipient device. The system can determine, prior to transmission of the electronic message for receipt by the recipient device, a category of the recipient device based on a domain name associated with an internet protocol address of the recipient device. The system can generate a plurality of content segments based on overlapping sequences of words in the electronic message. The system can identify, using machine learning models and based on the category of the recipient device, a security parameter to apply to the electronic message. The system can detect, using machine learning models, an incompatibility between a content segment and the security parameter. The system can block, responsive to the detection of the incompatibility, the transmission of the electronic message for receipt by the recipient device.