Real-Time Email Misdirection Detection and DLP Notification
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Solution Overview
Problem
Organizations face challenges in preventing data loss due to misdirected emails, which can result in financial losses, loss of brand reputation, and productivity losses, and existing solutions struggle to balance prevention with user experience.
Innovation Solution
A computing platform that detects input of a target recipient domain into an email message, identifies unintended recipient domains in real time, and sends notifications to prevent data loss by analyzing the email message against data loss prevention rules and using machine learning techniques to determine intended recipients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated processes are used to prevent misdirected emails, then data loss prevention is improved, but user experience deteriorates
Solution Approach 1:
The system performs preliminary analysis of email content, recipient addresses, and contextual patterns before the email is sent. Machine learning models evaluate the likelihood of misdirection and DLP violations in advance, allowing the system to intervene proactively rather than reactively, thereby preventing data loss while minimizing disruption to user workflow
Solution Approach 2:
The system provides real-time feedback to users through notifications that indicate potential misdirection or DLP violations. This feedback mechanism allows users to review and correct issues before sending, maintaining user control and experience while improving data loss prevention effectiveness through collaborative human-machine operation
2Measurement precision
If real-time analysis of email messages is performed, then misdirected email detection is improved, but system complexity increases
Solution Approach 1:
The system divides the complex analysis task into separate functional modules: machine learning models for misdirection detection, DLP rule engines for content analysis, and notification systems for user interaction. This segmentation allows each component to specialize in specific detection tasks, improving overall precision while managing complexity through modular architecture
Solution Approach 2:
The computing platform is designed as a multi-functional system that handles misdirected email detection, DLP rule validation, machine learning analysis, and user notification within a single integrated architecture. This universal approach consolidates complexity into one platform rather than requiring multiple separate systems, improving detection capabilities while controlling overall system complexity
3Reliability
If multiple data loss prevention rules are applied, then data protection is improved, but email processing time increases
Solution Approach 1:
The system applies DLP rules selectively rather than universally to all emails. Machine learning models first assess the risk level and content characteristics of each email, then apply appropriate subsets of DLP rules accordingly. This partial action approach ensures comprehensive data protection for high-risk emails while reducing processing time for low-risk communications
Data Source
AI summary
Aspects of the disclosure relate to data loss prevention. A computing platform may detect input of a first target recipient domain into a first email message. The computing platform may identify, in real time and prior to sending the first email message, that the first target recipient domain is an unintended recipient domain instead of an intended recipient domain. The computing platform may identify, in real time and prior to sending the first email message, that the first email message violates one or more data loss prevention rules. Based on identifying the violation, the computing platform may send a notification that the first target recipient domain is flagged as an unintended recipient domain and one or more commands directing a user device of the message sender to display the notification.