Email Protection Module With HA Fail-Open DLP Control
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Solution Overview
Problem
Conventional rule-based data loss prevention (DLP) systems struggle with scalability, context awareness, and high false positive/negative rates, particularly in managing outbound email messages, and lack user engagement for effective data loss prevention.
Innovation Solution
A cyber security appliance with an email protection module that conducts pattern-of-life analyses, integrates AI models, and includes a High Availability (HA) fail-open control logic to prevent data loss by analyzing email content in real-time, providing feedback to users, and automatically responding to potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based DLP systems are used to monitor and protect sensitive information in email messages, then data loss prevention capability is provided, but the systems produce high rates of false positives and false negatives due to static rules that cannot adapt to dynamic data usage patterns
Solution Approach 1:
The patent implements dynamic rule generation by continuously learning from email message patterns and user behaviors. The system adapts rules in real-time based on observed data flows, replacing static predefined rules with dynamically generated rules that evolve with changing organizational patterns, thereby reducing false positives and false negatives while maintaining adaptability.
Solution Approach 2:
The system performs self-learning by automatically analyzing email message content, patterns, and user behaviors to generate and refine its own detection rules without requiring manual intervention. This self-service capability enables the system to continuously improve its accuracy by learning from organizational-specific data usage patterns.
2Reliability
If numerous static rules are created and maintained in rule-based DLP systems, then comprehensive data coverage is achieved, but the complexity of managing and updating rules becomes resource-intensive and error-prone
Solution Approach 1:
The system automatically generates, refines, and maintains detection rules through continuous learning from email patterns and user behaviors. This eliminates the manual burden of creating and updating numerous static rules while maintaining comprehensive data coverage, as the system self-adjusts its rule set based on observed organizational patterns.
Solution Approach 2:
The system dynamically adjusts rule parameters based on learned patterns from email message content, user behaviors, and organizational data flows. Instead of managing fixed rule sets, the system modifies detection parameters in real-time to optimize coverage while reducing management complexity through automated adaptation.
3Reliability
If rule-based DLP systems monitor all email messages against predefined rules, then potential data loss is identified, but the lack of context awareness makes it difficult to differentiate between legitimate business activities and potential data loss
Solution Approach 1:
The system incorporates feedback loops that continuously learn from email message patterns, user behaviors, and contextual information. By analyzing feedback from legitimate business communications and actual data loss events, the system refines its contextual understanding to better distinguish between authorized and unauthorized data activities, reducing false positives while maintaining detection accuracy.
Solution Approach 2:
The system automatically develops contextual awareness by learning from organizational email patterns and user behaviors without manual configuration. It self-adjusts its understanding of legitimate versus suspicious activities by continuously analyzing message content, recipients, timing, and communication patterns specific to the organization.
4Reliability
If conventional email security modules use predefined rules to stop outgoing email messages, then data loss prevention is provided, but the rules are either too permissive increasing data loss risk or too restrictive interrupting legitimate business activity
Solution Approach 1:
The system dynamically adjusts its prevention thresholds and rules based on learned organizational patterns and user behaviors. Instead of using fixed permissive or restrictive rules, the system adapts its detection sensitivity in real-time to match the organization's specific communication patterns, ensuring that legitimate business activity flows smoothly while effectively preventing actual data loss.
Solution Approach 2:
The system automatically learns and adapts to the organization's legitimate business communication patterns through continuous monitoring and analysis. This self-learning capability enables the system to distinguish between normal business email traffic and actual data loss attempts, adjusting its prevention actions accordingly to avoid interrupting legitimate activities while maintaining strong protection.
Data Source
AI summary
Implemented within a cyber security appliance, a non-transitory storage medium configured to store software that, when executed, conducts data loss prevention evaluation of an email message to protect against exfiltration of sensitive data from an enterprise. The software includes an email protection module and high availability (HA) fail-open control logic. The email protection module includes email threat detection logic to analyze content associated with an outbound or lateral email message for potential data loss characteristics. The HA fail-open control logic is configured to (i) detect operational failure of the email protection module or intake disruption of email messages via an Application Programming Interface (API) providing access to the email protection module and (ii) redirect the email messages to HA cloud infrastructure pertaining to the enterprise for temporary storage and subsequent release of the redirected email messages upon detecting the operational failure or the intake disruption.