Multi-Level Data Loss Prevention System for Interconnected Networks
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Solution Overview
Problem
Current data loss prevention (DLP) systems are perimeter-oriented and reactive, failing to prevent attacks on modern, highly interconnected networks, which are subject to multiple fronts of data breaches, and lack proactive measures for identifying and mitigating cybersecurity threats before data loss occurs.
Innovation Solution
A comprehensive data loss prevention and compliance management system that employs a combination of human, device, and organizational level monitoring, using behavioral analytics, natural language processing, and machine learning to identify anomalies, assign risk scores, and generate alerts, with a rules engine for proactive data classification and analysis, enabling real-time threat detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If perimeter-oriented DLP approaches are used, then network security boundaries are established, but they fail to prevent attacks on modern highly interconnected networks that are subject to multiple fronts of data breaches
Solution Approach 1:
The patent segments the network monitoring approach into multiple levels: endpoint level (device monitoring), user level (human activity monitoring), and network level. This segmentation allows the system to move away from a single perimeter-oriented approach to a distributed monitoring architecture that can detect threats at multiple points in the network, thereby resolving the contradiction between maintaining security boundaries and adapting to modern interconnected networks.
Solution Approach 2:
The patent introduces a new dimension of monitoring by implementing multi-level surveillance that operates simultaneously at endpoint, user, and network levels. This dimensional expansion transforms the traditional two-dimensional perimeter security model into a three-dimensional monitoring space, enabling the system to detect and prevent data breaches from multiple fronts while maintaining adaptability to modern network architectures.
2Measurement precision
If reactive forensic analysis is performed after data loss occurs, then post-event determination and identification can be made, but the data loss has already occurred and cannot be prevented
Solution Approach 1:
The patent implements preliminary action by continuously monitoring and analyzing user behavior patterns before actual data breaches occur. The system establishes baseline behavioral profiles and detects anomalies in real-time, allowing security personnel to intervene and prevent data loss before it happens. This shifts the approach from reactive forensic analysis to proactive threat prevention, eliminating the time loss between breach and detection.
Solution Approach 2:
The patent incorporates continuous feedback loops where monitoring data is immediately analyzed and used to adjust security responses. The system provides real-time feedback on detected anomalies and potential threats, enabling immediate corrective action rather than waiting for post-event forensic analysis. This feedback mechanism reduces the time from breach to detection while maintaining high measurement precision through continuous behavioral analysis.
3Reliability
If zero trust architecture with point-to-point encryption is implemented, then authorized data exchanges are protected, but typical perimeter oriented DLP approaches have no or limited ability to review this type of traffic
Solution Approach 1:
The patent introduces an intermediary approach by implementing monitoring at the endpoint and user levels rather than attempting to inspect encrypted traffic in transit. The system uses behavioral analytics and device monitoring as intermediaries to detect threats without needing to decrypt or directly review encrypted point-to-point traffic, thereby maintaining both the security benefits of zero trust architecture and the ability to detect and prevent data breaches.
Data Source
AI summary
A system and method to identify and prevent cybersecurity attacks on modern, highly-interconnected networks, to identify attacks before data loss occurs, using a combination of human level, device level, system level, and organizational level monitoring.