Cognitive Computing Engine for Automated Infrastructure Mapping
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Solution Overview
Problem
Current methods for generating an infrastructure map of an enterprise environment are inefficient and prone to errors due to manual entry, vendor-specific data formats, and lack of context, leading to hidden network vulnerabilities and slow incident resolution.
Innovation Solution
A layered approach using artificial intelligence techniques for data abstraction, normalization, and relationship discovery, which ingests configuration files, harmonizes data, and stores relationships in a database, enabling machine learning models to automatically generate and maintain an accurate infrastructure map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual entry methods are used to generate infrastructure maps, then flexibility in handling vendor-specific data formats is maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The system enables automated self-service map generation by having assets automatically publish their own information through standardized interfaces. The cognitive computing engine autonomously ingests data from multiple sources, normalizes vendor-specific formats, and generates infrastructure maps without human intervention, resolving the contradiction between manual flexibility and automated efficiency
Solution Approach 2:
The patent implements a universal standardized data format that can handle multiple vendor-specific data formats. The cognitive computing engine serves multiple functions: ingesting data from various sources, normalizing different formats, discovering relationships, and generating maps, thereby achieving both versatility in format handling and automated efficiency
2Reliability
If manual entry methods are used to generate infrastructure maps, then adaptability to different data formats is maintained, but accuracy and reliability of the maps deteriorate due to human errors
Solution Approach 1:
The patent introduces a cognitive computing engine as an intermediary between raw vendor-specific data and the final infrastructure map. This intermediary automatically normalizes data from multiple vendors into a standardized format, discovers relationships between assets, and validates data consistency, thereby improving reliability while managing complexity through automated intelligence rather than manual processes
Solution Approach 2:
The system transforms vendor-specific data parameters into standardized parameters through automated normalization. The cognitive computing engine changes the state of data from unstructured, vendor-specific formats to structured, standardized formats with consistent relationships, improving map accuracy while the automated parameter transformation manages the complexity of handling multiple data formats
3Difficulty of detecting and measuring
If traditional mapping methods are used, then simplicity of the process is maintained, but the ability to identify network vulnerabilities and dependencies is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual mapping methods with cognitive computing and machine learning systems. The cognitive computing engine automatically ingests data, normalizes formats, discovers relationships between assets, and identifies vulnerabilities and dependencies. This substitution of automated intelligence for manual processes enhances vulnerability detection capability while the modular architecture manages the complexity of the analysis system
Data Source
AI summary
A method may include ingesting a plurality of sources files from a plurality of infrastructure assets; inputting the plurality of source files into a cognitive computing engine (CCE); receiving an output from the CCE, the output indicating a plurality of relationships between the infrastructure assets; and updating a data store based on the plurality of relationships.