Development of a Graph-Based Network Discovery Tool for Vendor Security Assessment

The client faces challenges in assessing the security levels of its numerous vendors, which impacts overall corporate cybersecurity. Existing manual processes are inefficient and inadequate to map complex vendor relationships and identify potential security vulnerabilities within their interconnected networks.

A mid-sized enterprise specializing in managing vendor and partner networks for corporate security compliance.

• Develop a proof-of-concept for an automated network discovery and modeling system to visualize vendor relationships.
• Automate the collection and storage of internet-derived data related to company entities and their interconnections.
• Enable visualization and analysis of complex network graphs to identify high-risk vendor relationships.
• Improve the speed and accuracy of security assessments through automation, reducing manual effort over time.

• Internet crawling module to gather data about companies and their network connections.
• Graph database integration to store and manage complex relationship data efficiently.
• Automated data parsing and normalization for consistency.
• Visualization dashboards to interpret network structures and identify vulnerabilities.
• Reporting tools for ongoing security posture monitoring.

• Internet data sources and web crawling APIs for data acquisition
• Existing security data analysis tools or dashboards for comprehensive reporting

• System should support crawling and data processing at scale, capable of handling thousands of entities and relationships efficiently.
• Real-time or near-real-time data updates to ensure current network models.
• High availability and fault tolerance for continuous operation.
• Data security and compliance with relevant data privacy standards.

The deployment of this automated network discovery and modeling system is expected to significantly improve the client’s ability to identify security vulnerabilities within vendor networks. It aims to reduce manual assessment efforts, increase detection accuracy, and strengthen overall cybersecurity posture, contributing to better risk management and regulatory compliance.