Holistic IT Infrastructure Model Using KG and RAG Pre-Training
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Solution Overview
Problem
Existing AI approaches for IT infrastructure data modeling are limited by their focus on specific data modalities and lack the ability to handle fragmented, siloed, and heterogeneous sources such as OS, network, monolith, and microservice-based applications, and existing technologies fail to provide a scalable and general-purpose solution for modeling cybersecurity and automation.
Innovation Solution
The proposed solution is the LITHIUM (Large Information Technology Holistic Infrastructure) model, which encapsulates both natural language and machine readable IT information, utilizing a Knowledge Graph (KG) representation, and employs intrinsic Retrieval Augmented Generation (RAG) mechanisms to populate implicit context within the IT infrastructure, and provides a general-purpose approach that encapsulates both natural language and machine readable IT information, utilizing a Retrieval Augmented Generation (RAG) mechanism to populate implicit context within the IT infrastructure, and provides a flexible database that integrates through a Knowledge Graph (KG) for simplicity of exposition, and enhances the structure and operation of the IT infrastructure, enabling cybersecurity and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing AI approaches focus on specific data modalities, then they can achieve deep specialization in that modality, but they cannot handle fragmented and heterogeneous IT infrastructure data from multiple sources
Solution Approach 1:
The patent creates a universal infrastructure model that can process multiple data modalities (natural language, machine readable IT information, OS data, network data, application data) through a single unified framework. The model uses a common architecture with modality-specific processing layers that feed into shared representation learning components, enabling one system to handle diverse IT infrastructure data types without requiring separate specialized models for each data source.
2Reliability
If statistical learning and inference are heavily used to achieve mature defensive postures, then cybersecurity capability is improved, but scalability and automation capability are limited
Solution Approach 1:
The patent replaces traditional statistical learning and rule-based inference mechanisms with a neural network-based infrastructure model. This neural model automatically learns patterns and relationships from heterogeneous IT infrastructure data, substituting manual security configuration and rule-based detection with automated, scalable neural inference that maintains high reliability while enabling broader scalability and productivity.
3Reliability
If human expertise is leveraged for cybersecurity domain knowledge, then subject matter expertise is captured, but general-purpose AI development is limited
Solution Approach 1:
The patent performs preliminary action by pre-training the neural infrastructure model on extensive IT infrastructure data from multiple modalities before deployment. This pre-training phase captures domain expertise and patterns in advance, allowing the model to be deployed as a general-purpose system that can adapt to specific cybersecurity tasks without requiring manual encoding of expert knowledge for each application scenario.
Data Source
AI summary
One example method is for constructing and pre-training a model of an information technology (IT) infrastructure, and includes creating a model of the IT infrastructure by, generating a KG (knowledge graph) representation of IT infrastructure data, serializing information flows within the KG to generate serialized information flows, and pre-processing the serialized information flows to improve a quality of the IT infrastructure data relative to a quality of the IT infrastructure data prior to the pre-processing, and pre-training the model, including training the model to capture a structure of both natural language and IT infrastructure flow by predicting, without hallucinations, tokens and identities of the IT infrastructure, providing customizations to enable the model to correctly predict the identities, and pre-training the model with causal modeling when identities are unknown in one of the information flows.