Sentiment Analysis for Dynamic Data Protection Policy Adjustment
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Solution Overview
Problem
Data protection systems fail to effectively adapt to real-time cyber threats, as they are not aware of current risks, leading to inadequate protection of both primary and backup data storage systems.
Innovation Solution
The system assesses sentiment from darknet forums using Natural Language Processing (NLP) to evaluate cyber threats and dynamically adjust data protection policies, including isolating backup storage systems through an air gap mechanism when high risks are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data protection systems use static protection policies, then system simplicity is maintained, but the ability to respond to real-time cyber threats is insufficient
Solution Approach 1:
The patent implements dynamic data protection policies that automatically adjust based on real-time sentiment analysis of cyber threat intelligence. The system transitions from static protection levels to dynamic adjustment, where backup storage systems can be isolated or protected based on current threat conditions detected through NLP analysis of darknet forums and threat intelligence sources.
Solution Approach 2:
The system incorporates feedback loops where sentiment analysis results from threat intelligence sources continuously inform policy adjustments. The sentiment analysis engine monitors cyber threat discussions and feeds this information back to the data protection system, which then adjusts protection policies accordingly, creating a closed-loop adaptive protection mechanism.
2Reliability
If backup storage systems remain connected to the network, then data accessibility is maintained, but vulnerability to cyber attacks increases
Solution Approach 1:
The system performs preliminary isolation of backup storage systems based on predicted or detected threat levels before attacks can compromise the data. By proactively isolating backup systems when threats are detected in the sentiment analysis, the system prevents potential attacks while maintaining normal network connectivity during low-threat periods.
Solution Approach 2:
The patent introduces an intermediary sentiment analysis engine that acts as a mediator between threat intelligence sources and the data protection system. This intermediary analyzes cyber threat sentiment and translates it into appropriate protection actions, allowing the system to respond to threats without direct exposure while maintaining operational flexibility.
3Measurement precision
If sentiment analysis from darknet forums is implemented, then awareness of current cyber threats is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent replaces manual threat assessment and analysis with automated Natural Language Processing (NLP) systems. Instead of human analysts manually monitoring darknet forums and assessing threats, the system uses NLP engines to automatically scrape, analyze, and interpret sentiment from threat intelligence sources, significantly reducing operational complexity while improving detection accuracy.
Solution Approach 2:
The sentiment analysis system performs self-service by automatically collecting, analyzing, and acting on threat intelligence without requiring continuous human intervention. The NLP engine autonomously monitors multiple sources, processes sentiment data, and triggers appropriate protection responses, making the threat detection process self-sufficient while maintaining high accuracy.
Data Source
AI summary
Systems and methods for providing data protection operations including cyber-threat protection operations. A sentiment analysis may be performed using language analysis to identify or determine a general or specific sentiment with or without intent to do harm. A score of the sentiment is then determined to assess risk. The data backup policy can be updated based on the assessed risk.