IT GenOps Data Privacy via Dynamic Schemas and FDW Querying
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Solution Overview
Problem
Current IT observability and monitoring practices are resource-intensive, complex, and inefficient due to the management of vast and diverse data sources, leading to delayed insights and increased operational costs, with existing tools and methods failing to provide real-time monitoring and proactive issue resolution.
Innovation Solution
The implementation of IT Generative Operations (IT GenOps) utilizing Large Language Models (LLMs) and dynamic schematization, which includes structured vector augmented generation and Foreign Data Wrappers (FDWs) to streamline data processing, reduce resource consumption, and enhance accuracy by providing real-time insights and actionable recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional IT observability and monitoring tools are used to manage vast and diverse data sources, then monitoring coverage is improved, but resource consumption and operational costs increase significantly
Solution Approach 1:
The patent extracts only the essential and relevant data from vast data sources using targeted queries generated by LLMs, rather than collecting and processing all available data. This selective extraction approach maintains comprehensive monitoring coverage while significantly reducing the computational resources needed for data processing and analysis.
Solution Approach 2:
The system dynamically changes query parameters and data selection criteria based on current system state and identified issues. LLMs generate adaptive queries that adjust which data sources are accessed and what parameters are monitored, optimizing resource usage while maintaining effective monitoring coverage.
2Measurement precision
If comprehensive data collection from multiple IT platforms is implemented, then monitoring accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal LLM-based interface that can query and analyze data from multiple different IT platforms (cloud services, on-premises systems, containers, servers) through a single unified system. This multi-functional approach maintains monitoring accuracy across diverse platforms while reducing system complexity by eliminating the need for separate monitoring tools for each platform.
Solution Approach 2:
The LLM acts as an intermediary layer between the user and multiple IT platforms. It translates natural language queries into platform-specific queries and consolidates results, maintaining monitoring accuracy while shielding users from the complexity of interacting with multiple different systems directly.
3Speed
If real-time analysis of vast data volumes is performed, then issue detection speed is improved, but computational resources required increase
Solution Approach 1:
The system performs partial analysis by focusing computational resources on analyzing only the specific data subsets relevant to identified issues or questions. LLMs generate targeted queries that retrieve and analyze only necessary data portions rather than processing entire data volumes, maintaining fast issue detection while reducing computational resource requirements.
4Reliability
If specialized personnel are hired to manage complex monitoring systems, then monitoring expertise is improved, but operational costs increase
Solution Approach 1:
The patent implements self-service monitoring capabilities where LLMs automatically generate queries, analyze data, and provide insights without requiring specialized human intervention. The system serves itself by autonomously navigating complex IT environments, querying multiple platforms, and synthesizing results, maintaining high monitoring expertise while eliminating the need for expensive specialized personnel.
Solution Approach 2:
The system replaces the mechanical need for human experts with an automated LLM-based intelligence system. The LLM performs cognitive tasks previously requiring specialized personnel (query generation, data analysis, insight synthesis) through software intelligence, maintaining expert-level monitoring capabilities while dramatically reducing operational costs associated with hiring and training specialized staff.
Data Source
AI summary
Techniques and mechanisms are provided for maintaining data privacy in the context of Information Technology Generative Operations (IT GenOps). Enterprise curated data and selected telemetry data from various IT platforms is secured in a dynamically schematized database in response to an IT administrator query. In some examples, Foreign Data Wrappers (FDWs) are used to dynamically schematize select data into structured virtual tables accessible by Large Language Models (LLMs). This allows for advanced analysis and querying without exposing confidential data. Mechanisms to enhance data privacy, reduce operational inefficiencies, and improve the accuracy of insights generated by LLMs are provided, to ensure that enterprise data remains secure while enhancing IT Operations.