AI Email Screening for Inadvertent Data Disclosure Risk
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Solution Overview
Problem
Existing methods for preventing inadvertent data disclosures (IDDs) in outbound emails, such as those involving incorrect attachments, unsecured data, or wrong recipients, are inefficient and prone to human error, leading to reputational damage and loss of business.
Innovation Solution
Employing artificial intelligence techniques, including multi-hierarchical clustering and behavioral models, to analyze outbound emails for IDD risks, applying rules, and performing pre-checks to identify and mitigate potential IDD issues, with options for blocking, alerting, or modifying emails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review methods are used to detect IDD in outbound emails, then human reviewers can identify potential issues, but the process is inefficient and prone to human error
Solution Approach 1:
The patent replaces manual human review with an automated AI system that uses machine learning models to analyze outbound emails. The system processes email content, attachments, and metadata automatically, eliminating human error while maintaining high detection accuracy and significantly improving processing efficiency.
Solution Approach 2:
The AI system performs self-learning and self-improvement by continuously analyzing historical email data and IDD patterns. The model automatically updates its detection capabilities without requiring manual retraining, enabling the system to adapt to new disclosure patterns while maintaining consistent performance.
2Productivity
If automated rule-based systems are used to filter emails, then processing efficiency improves, but the systems lack the ability to detect complex or novel IDD patterns
Solution Approach 1:
The system dynamically adjusts detection parameters and thresholds based on learned patterns from historical data. The AI model can adapt its sensitivity and focus to different types of disclosures (e.g., attachments vs. email body vs. recipient lists), enabling versatile detection across multiple IDD scenarios while maintaining efficient processing.
Solution Approach 2:
The detection system transitions from static rule-based filtering to dynamic AI-driven analysis that can adapt to changing disclosure patterns. The model continuously learns from new data, allowing it to detect emerging IDD patterns while maintaining high processing speeds through optimized inference mechanisms.
3Reliability
If comprehensive manual review of all outbound emails is performed, then IDD detection coverage is maximized, but the time and resources required increase significantly
Solution Approach 1:
The AI system applies selective analysis to emails based on risk scoring, focusing comprehensive review resources on high-risk messages while using lighter analysis for low-risk emails. This partial action approach maintains high detection coverage for critical disclosures while significantly reducing average processing time across all emails.
Solution Approach 2:
The system provides continuous monitoring and detection capabilities without requiring complete manual review of each email. The AI model operates continuously in the background, analyzing emails in real-time as they are composed or queued for sending, eliminating the need for time-consuming batch processing or manual intervention.
Data Source
AI summary
Exemplary embodiments may employ artificial intelligence models to identify outbound emails (e.g., those directed to recipients that are outside an organization, a business unit or other partition of an organization) that are at risk of containing IDDs. The identified emails may be automatically blocked and/or may be forwarded to a reviewer for further scrutiny and/or remediation. The reviewer may review the emails and determine whether the emails should be blocked from being sent and/or whether the email should be remediated. The identified emails may be returned to their senders or associated business unit or organization, and the senders or remediating parties may modify the emails and attempt to send out the modified emails.