Real-time Data Transmission Security Classification
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Solution Overview
Problem
Large enterprise organizations face challenges in real-time monitoring and classification of data transmissions across complex networks, leading to potential security threats due to the difficulty in manually managing vast network users and devices with continuous data flow, necessitating automated solutions for accurate and timely security classification and remediation.
Innovation Solution
A computing platform with machine learning capabilities detects and analyzes data transmissions, determines security classifications, and triggers appropriate actions, including modifying security profiles and preventing unauthorized data transmissions, to ensure compatibility between source and destination applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and classification of data transmissions is performed, then security classification accuracy may be improved, but the complexity of managing vast network users and devices increases significantly
Solution Approach 1:
The system enables automated self-service through machine learning models that automatically detect, analyze, and classify security levels of data transmissions without human intervention. The ML models process network traffic independently, assigning security classifications based on learned patterns from training data, thereby eliminating the need for manual monitoring while maintaining high classification accuracy.
2Loss of time
If real-time analysis of data flow is performed, then timely security remediation is achieved, but network resources and bandwidth utilization are consumed
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models with extensive security classification data before deployment. This pre-training phase allows the models to learn security patterns offline, enabling them to make rapid real-time classifications without requiring extensive computational resources during actual network monitoring. The preliminary training prepares the system to quickly identify and respond to security threats as they occur.
3Productivity
If automated machine learning models are deployed for security classification, then productivity and speed of classification are improved, but the complexity of the system increases
Solution Approach 1:
The system introduces machine learning models as intermediary components between network traffic and security classification decisions. These ML models act as intelligent mediators that automatically analyze data transmissions and determine security levels, bridging the gap between raw network traffic and security policies. This intermediary layer handles the complexity of pattern recognition and classification, simplifying the overall system architecture while maintaining high productivity.
Data Source
AI summary
Aspects of the disclosure relate to real-time classification of content in a data transmission. A computing platform may detect, in real-time and via a computing device, a plurality of data transmissions between applications over a communications network. Then, the computing platform may retrieve, for a particular data transmission of the plurality of data transmissions, a content of the particular data transmission. The computing platform may then analyze, via the computing device, the content. Subsequently, the computing platform may determine, in real-time via the computing device and based on the analyzing, a security classification for the content. Then, the computing platform may cause, in real-time via the computing device, the content to be marked with the determined security classification.