Pre-fetch Engine Security for Mesh Data Networks
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Solution Overview
Problem
Large enterprise organizations face inefficiencies and latency in retrieving data from multiple geographic locations, compounded by differing regulatory requirements for data transmission, which complicates data processing operations.
Innovation Solution
A pre-fetch engine utilizing a machine learning model generates a pre-fetch template to identify responsive data sets, transmitted via a mesh data transmission network, with data controllers at each location evaluating and modifying data for compliance with local regulations, and handling data from both trusted and untrusted sources by validating and quarantining as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is retrieved from multiple geographic locations in real-time, then data processing can be performed, but latency increases and retrieval efficiency decreases
Solution Approach 1:
The pre-fetch engine proactively identifies and retrieves data sets from multiple geographic locations before they are actually requested by data processing operations. By performing data retrieval in advance and storing it in a centralized location, the system eliminates retrieval latency when the data is needed, while the machine learning model ensures only relevant data is pre-fetched to maintain efficiency.
2Reliability
If all bulk data is retrieved from data sources, then complete data availability is achieved, but data transmission volume increases and processing overhead increases
Solution Approach 1:
The machine learning model analyzes data requests to identify only the specific portions of bulk data that are actually needed for processing. Instead of retrieving entire data sets or over-fetching data, the system precisely targets and retrieves only the relevant subsets, reducing transmission volume while ensuring complete availability of necessary data for processing operations.
3Ease of operation
If data transmission is allowed across all geographic regions, then data accessibility is improved, but compliance with local regulatory requirements becomes difficult to maintain
Solution Approach 1:
The system introduces a centralized pre-fetch engine and machine learning model as intermediaries between data sources in multiple geographic locations and data processing operations. This intermediary architecture evaluates data requests, identifies relevant data sets, and retrieves them through a mesh network, thereby maintaining data accessibility while centralizing control to simplify regulatory compliance evaluation and enforcement.
4Productivity
If data is pre-fetched from untrusted sources, then data availability is improved, but security risks and data validation requirements increase
Solution Approach 1:
The system performs preliminary security evaluation and validation of data sets from untrusted sources during the pre-fetching process, before the data is made available for processing. The machine learning model identifies potentially malicious or non-compliant data, and the system quarantines suspicious data sets for further inspection, thereby enabling fast retrieval from untrusted sources while maintaining security through advance evaluation rather than reactive measures.
Data Source
AI summary
Arrangements for controlling data retrieval are provided. In some aspects, a data request may be received by a computing platform. A pre-fetch engine may be executed to analyze the data request and generate, using a machine learning model, a pre-fetch template identifying data sets responsive to the request. The pre-fetch template may be transmitted to one or more data repositories and response data including one or more of the identified data sets may be received. The source of the data sets may be evaluated to determine whether it is a trusted source. If the data repository is a trusted source, the data may be processed. If the data repository is not a trusted source, the data may be analyzed to validate the data and determine whether any anomalies exist. If the data is validated, the data may be processed. If the data is not validated, the data may be quarantined.