Cognitive Screening of Attachments for Risk Mitigation
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Solution Overview
Problem
The accidental sharing of sensitive information during electronic communications poses a significant reputational and financial risk to organizations, as existing technologies lack effective mechanisms to prevent the inadvertent transmission of confidential data.
Innovation Solution
A computer-implemented method leveraging machine learning to determine the context and historical context of electronic communications, assign risk scores, and execute action plans to prevent the sharing of sensitive information by prompting users to remove or reconsider attaching sensitive documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to determine context and historical context of electronic communications, then the precision of risk assessment is improved, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary analysis by determining the context of electronic communications and historical context before transmission occurs. Machine learning models pre-process communication data, attachment patterns, and user behavior to establish baseline risk assessments, enabling proactive prevention rather than reactive detection.
Solution Approach 2:
The patent introduces an intermediary security system that sits between the user and the transmission process. This intermediary layer analyzes communications, evaluates risk scores, and mediates whether transmissions proceed or are blocked, adding precision without requiring direct modification of core communication systems.
2Reliability
If risk scores are calculated and assigned to electronic communications, then the reliability of security screening is improved, but the time required for processing increases
Solution Approach 1:
The system applies partial action by focusing risk assessment on specific high-risk elements such as attachments and contextual factors rather than analyzing every single communication uniformly. Low-risk communications receive streamlined processing while high-risk items undergo more thorough analysis, balancing reliability with processing speed.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on risk levels. Communications are evaluated across multiple parameters (attachment type, recipient, user behavior, historical patterns), and the system adapts the depth of analysis based on initial parameter assessments, optimizing the balance between screening reliability and processing time.
3Object-affected harmful factors
If action plans are executed to prevent sharing of sensitive information, then the protection of confidential data is improved, but the ease of operation decreases
Solution Approach 1:
The system provides real-time feedback to users when risk scores exceed thresholds. Action plans are communicated back to users with clear explanations of why a transmission is blocked and what changes are needed. This feedback loop maintains security while guiding users toward safe operations without completely preventing legitimate communications.
Solution Approach 2:
Instead of requiring users to prove their communications are safe, the system inverts the approach by assuming communications are safe unless risk indicators are present. This reverses the burden of proof and reduces unnecessary interventions, maintaining ease of operation for legitimate users while still protecting confidential data.
Data Source
AI summary
An approach for cognitively processing documents to ameliorate inadvertent sharing of sensitive information during electronic communications is disclosed. The approach determines a first context and historical context of an electronic communication being prepared for transmission. The approach determines one or more risk scores based on the first context and the historical context. The approach assigns the one or more risk scores to the first context and the historical context and determining whether the one or more risk scores exceed one or more predetermined thresholds. The approach executes an action plan to prevent sensitive document from being transmitted should the risk score exceed the threshold.