AI-Assisted Network Audit System Using LLM Agents
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Solution Overview
Problem
Conventional network audits are manual, time-consuming, and prone to errors, requiring significant effort for data processing and report customization, which hampers efficiency and compliance in network management.
Innovation Solution
The implementation of an AI-assisted network audit system using a Large Language Model (LLM) based Network Copilot for automated data extraction, normalization, and report generation, featuring a user interface for uploading audit criteria, agent-based data collection, and modules for data correlation and normalization to produce unified audit reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual network audit methods are used, then flexibility in customizing audit criteria and reports is maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The system enables self-service automation where the audit platform automatically collects data from network devices, processes it through normalization and correlation modules, and generates compliance reports without requiring manual intervention for each audit task, thereby reducing time loss while maintaining accuracy through systematic automated procedures
Solution Approach 2:
Manual mechanical audit processes are replaced with an automated electronic system that uses software agents to collect data, algorithmic modules to normalize and correlate data, and automated report generation tools to produce audit results, eliminating human error and significantly reducing audit time
2Productivity
If automated data collection tools are used, then data collection efficiency improves, but manual processing and normalization effort increases
Solution Approach 1:
The processing system is segmented into distinct functional modules: data collection agents, normalization module with vendor-specific templates, correlation module, and report generation module. Each module handles a specific aspect of processing, making the complex automated processing manageable and maintainable while preserving high data collection efficiency
Solution Approach 2:
Normalization templates act as intermediaries between raw collected data and the correlation module. These templates standardize data from multiple vendors into a common format, automatically bridging the gap between diverse data sources and the analysis requirements without requiring manual intervention
3Adaptability or versatility
If manual report generation is performed, then report customization for different user personas is flexible, but time consumption and error risks increase
Solution Approach 1:
The report generation module is designed with universal templates that can serve multiple user personas (security teams, network architecture teams, compliance officers) with different reporting needs. A single automated system handles diverse report requirements by applying different templates and parameters, maintaining adaptability while eliminating manual generation time
Solution Approach 2:
Report templates and formatting rules are pre-configured for different user personas and compliance requirements. When an audit is executed, the system automatically selects and applies the appropriate pre-prepared templates, eliminating the need for manual report customization while maintaining flexibility for different stakeholders
4Device complexity
If traditional manual audit methods are used, then system simplicity is maintained, but productivity and compliance efficiency deteriorate
Solution Approach 1:
The automated audit system is divided into distinct functional segments (data collection agents, normalization module, correlation module, report generation module) that can be independently configured and maintained. This modular architecture manages complexity while enabling high automated productivity across all audit functions
Data Source
AI summary
In one general aspect, the method may include providing a user interface on a web server for users to upload audit criteria, where audit criteria further may include a mode of collection to by utilized by a collector module. Said method may also include installing a plurality of agents for automated data collection, where the agents are software modules controlled by an LLM network module. Said method may furthermore include receiving audit criteria from users in predefined template documents into the LLM network module. Said method may in addition include processing the audit criteria by the LLM network module and triggering automatic data collection by a collector to create collected data.