Machine Learning Data Security Prediction Platform
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Solution Overview
Problem
Current data security management approaches do not effectively analyze data to determine which portions require increased security, leading to wastage of compute resources by applying blanket security protocols to all data.
Innovation Solution
A data security management platform that uses machine learning algorithms, such as Random Forest, to predict the security levels of data portions, allowing for targeted application of security protocols only to critical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blanket security protocols are applied to all data, then security coverage is improved, but compute resource efficiency deteriorates
Solution Approach 1:
The patent applies different security protocols to different portions of data based on their predicted security levels. Machine learning models analyze data characteristics and assign specific security measures (e.g., encryption, access control) only to data portions requiring protection, rather than uniformly applying security to all data. This resolves the contradiction by maintaining comprehensive security coverage while optimizing compute resource usage through targeted security application.
Solution Approach 2:
The system dynamically adjusts security parameters (such as encryption strength, access control levels, and monitoring intensity) based on predicted security levels of different data portions. The machine learning models continuously evaluate data and modify security parameters in real-time, allowing the system to maintain high security where needed while reducing security overhead for low-risk data, thereby improving compute resource efficiency without compromising overall security coverage.
2Reliability
If security protocols are applied to all data, then security reliability is improved, but network performance deteriorates
Solution Approach 1:
The patent implements security protocols selectively based on data portion characteristics and predicted security levels. By applying security measures only to specific data portions that require protection rather than uniformly to all data, the system maintains security reliability for critical data while minimizing network overhead and improving overall network performance.
3Productivity
If machine learning prediction is added, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between data storage and security protocol application. These models analyze data characteristics and generate predictions about security requirements, serving as a mediator that determines which security protocols should be applied to which data portions. This intermediary layer improves resource efficiency by enabling targeted security application, while the use of established machine learning frameworks helps manage the added system complexity through modular and scalable architecture.
Data Source
AI summary
Techniques for management of data security are disclosed. For example, a method comprises collecting data from one or more devices, and predicting security levels of respective portions of the data using one or more machine learning algorithms. In the method, security configurations for a subset of the respective portions of the data are implemented based, at least in part, on corresponding predicted security levels of the subset of the respective portions.