Outbound Communication Scanning With Risk-Based Payload Sanitization
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Solution Overview
Problem
Outbound data communication often leads to unintended transmission of sensitive data, posing security risks, and existing global rules for prevention are resource-intensive and reduce operational efficiency.
Innovation Solution
A system and method that determines a risk profile for outbound communication, selects a scanning policy, and employs a secure machine learning model to sanitize or prevent transmission based on the risk profile, optimizing resource use and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If global rules are applied to prevent sensitive data transmission, then security is improved, but resource consumption increases and operational efficiency decreases
Solution Approach 1:
The patent implements risk-based scanning policies that apply different levels of inspection to different outbound communications based on their specific risk characteristics. Instead of uniform global rules, the system analyzes each communication's risk profile and applies appropriate scanning intensity, thereby maintaining security while reducing unnecessary resource consumption on low-risk transmissions.
Solution Approach 2:
The system dynamically adjusts scanning parameters and policy selection based on the determined risk profile of each outbound communication. By changing scanning parameters according to risk levels, the system optimizes the balance between security enforcement and resource efficiency, avoiding over-scanning of low-risk communications while maintaining rigorous inspection for high-risk ones.
2Productivity
If risk-based scanning policies are implemented, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the outbound communication inspection process into distinct phases: risk profile determination, scanning policy selection, and actual scanning execution. This segmentation allows the system to manage complexity by handling each phase separately with dedicated components, making the overall complex system more maintainable and manageable while achieving resource efficiency.
Solution Approach 2:
The system introduces an intermediary risk assessment layer between the outbound communication source and the scanning mechanism. This intermediary component determines risk profiles and selects appropriate scanning policies, acting as a mediator that simplifies the interaction between diverse communication types and the scanning infrastructure, thereby managing system complexity while improving resource efficiency.
3Measurement precision
If machine learning models are used for risk assessment, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial machine learning-based risk assessment by using ML models selectively for complex risk profile determinations rather than for every aspect of communication inspection. This partial application of ML technology maintains detection accuracy for critical assessments while avoiding the full processing time overhead of ML models for all scanning operations.
Data Source
AI summary
Systems and methods for securing data for outbound communication may include: determining a risk profile for the outbound communication based, the outbound communication comprising data modules including at least a payload, an origination endpoint, and a destination endpoint; determining a scanning policy from a plurality of scanning policies, based on the determined risk profile; determining a secure machine learning model from a plurality of secure machine learning models, based on the determined risk profile, wherein the secure machine learning model is determined based on an authentication level corresponding to the determined risk profile; providing one or more data modules to the secure machine learning model, based on the determined scanning policy; receiving a sanitized version of the payload based on an output of the secure machine learning model; and providing the sanitized version of the payload for the outbound communication.