Generative AI Knowledge Graph Interface for Cloud Anomaly Detection
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Solution Overview
Problem
Existing systems lack efficient methods for monitoring and analyzing data from cloud environments to detect anomalies and ensure data security, compliance, and asset management, particularly in complex networked systems involving multiple entities and regulatory requirements.
Innovation Solution
A data platform utilizing generative artificial intelligence to interface with a knowledge graph, which collects and processes data from cloud environments through agents, performs anomaly detection, and provides real-time insights for security, compliance, and asset management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data monitoring and analysis methods are used in cloud environments, then existing systems can collect and store data, but they lack efficient anomaly detection capabilities and real-time insights for security and compliance
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary structure that mediates between raw cloud environment data and anomaly detection algorithms. The knowledge graph organizes data into structured relationships (entities, attributes, relationships) that enable efficient pattern recognition and anomaly detection without requiring complex direct processing of raw data streams.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with generative AI models that can automatically learn patterns, detect anomalies, and generate insights from cloud environment data. The AI models process data through neural networks and machine learning algorithms, substituting conventional rule-based detection mechanisms with adaptive, learning-based systems.
2Loss of information
If comprehensive data collection from cloud environments is implemented, then more information is available for analysis, but network exposure and data processing burden increase
Solution Approach 1:
The patent extracts only the necessary data elements and relationships from cloud environments and stores them in the knowledge graph structure. Instead of collecting all available data, the system selectively extracts information related to entities, their attributes, and relationships that are relevant for anomaly detection, thereby reducing network exposure while maintaining information availability for analysis.
Solution Approach 2:
The patent segments data collection and processing into distinct stages: data extraction from cloud environments, structured storage in the knowledge graph, and targeted query processing. This segmentation allows the system to handle data in manageable portions rather than processing entire data streams, reducing network exposure and processing burden while maintaining comprehensive analysis capability.
3Reliability
If real-time anomaly detection is implemented, then security and compliance are improved, but data processing requirements and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-structuring data into the knowledge graph format and pre-computing relationships between entities. This preliminary organization of data enables faster anomaly detection queries without requiring intensive real-time computational processing, as the structured knowledge graph can be efficiently queried for specific anomaly patterns.
Solution Approach 2:
The patent changes the parameter representation of data from raw cloud environment formats to structured knowledge graph representations with standardized attributes and relationships. This parameter transformation enables more efficient storage, retrieval, and processing of data, reducing computational resource requirements while maintaining real-time anomaly detection capability.
Data Source
AI summary
Using generative artificial intelligence to interface with a knowledge graph, including: providing, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment; and providing a response to the request based on the knowledge graph and output from the generative AI model.