Data Leak Protection via ML Digital Fingerprints
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Solution Overview
Problem
Conventional data leak protection techniques rely on human-defined policies, leading to high false positives and inability to prevent data leaks across different communication platforms, especially in the high-volume electronic communication environments of modern organizations.
Innovation Solution
A data leak protection system utilizing machine learning to generate digital fingerprints for data packages, analyzing assets and contextual environments, and comparing them to domain-specific identifiers to assess potential data leaks, with a processing gateway triggering notifications and actions based on risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human-defined keyword policies are used to block confidential information, then data leak protection is provided, but false positives increase and legitimate communications are trapped
Solution Approach 1:
The patent replaces manual keyword-based filtering mechanisms with machine learning models that automatically learn and identify patterns of confidential information. The system uses trained ML models to analyze communication content, replacing the mechanical keyword matching process with intelligent pattern recognition that adapts to organizational-specific terminology and contexts, thereby reducing false positives while maintaining protection reliability
Solution Approach 2:
The system changes the parameters of detection by moving from static keyword strings to dynamic, context-aware patterns generated by machine learning. The ML models analyze multiple features including text content, metadata, communication patterns, and contextual relationships, transforming the detection approach from simple string matching to multi-parameter pattern recognition that accurately distinguishes confidential from non-confidential communications
2Measurement precision
If keyword-based policies are updated by human operators, then some accuracy improvement is achieved, but the system remains subject to high false positives and cannot scale to multiple platforms
Solution Approach 1:
The patent implements a universal machine learning-based detection system that functions across multiple communication platforms including email, instant messaging, collaboration tools, and cloud storage. The ML models are platform-agnostic and can be deployed across diverse communication channels, providing consistent confidential information detection regardless of the platform used, thereby achieving both accuracy and multi-platform adaptability
Solution Approach 2:
The system employs self-learning machine learning models that automatically improve detection accuracy without requiring continuous manual intervention. The models are trained on organizational data and continuously adapt to new patterns of confidential information, eliminating the need for operators to manually update keyword lists while maintaining high detection accuracy across evolving communication landscapes
3Reliability
If traditional intrusion detection systems are used, then some security monitoring is provided, but the system cannot identify domain-specific contexts and data perimeters
Solution Approach 1:
The patent applies local quality by training and deploying domain-specific machine learning models for different organizational contexts such as healthcare, finance, and legal sectors. Each domain receives customized ML models that understand industry-specific terminology, regulations, and communication patterns, enabling the system to detect confidential information with contextual awareness tailored to each domain's unique characteristics while maintaining overall security monitoring reliability
Data Source
AI summary
A data leak protection system and methods thereof are described that identify and analyze a digital fingerprint for a data package, the digital fingerprint characterizing the data package based on a corpus of data within the data package. In one embodiment, an asset descriptor is configured to identify one or more assets within the corpus of data while a contextual analyzer frames the one or more assets into the prevailing contextual environment. Then, a domain identifier further identifies a data perimeter based on the assets identified for the prevailing contextual environment. A comparison of the digital fingerprint to a collection of domain specific identifiers allows further actions responsive to a digital fingerprint falling outside of the data perimeter for an identified contextual environment. In one example, a data leak triggers quarantining of the data package for further manual processing.