Global Cybersecurity Leader Accelerates Real-Time Threat Detection with Connected Intelligence
The Challenge
Legacy SQL Server infrastructure could no longer classify emerging websites fast enough to keep pace with evolving cyber threats. The company needed real-time relationship analysis across massive datasets while supporting thousands of website classifications per second.
The Solution
Implemented TigerGraph to power real-time graph analytics across websites, domains, URLs, and threat signals, enabling machine learning to identify malicious relationships and classify emerging threats faster and more accurately at internet scale.
The Results
Improved real-time threat detection by supporting thousands of website classifications per second, continuously expanding one of the industry’s largest URL intelligence databases while delivering faster, more accurate protection against emerging cyber threats.
The Challenge
A market leader in cyber resilience provides cloud-based threat intelligence, endpoint protection, and disaster recovery services to organizations worldwide. Operating at internet scale, one of its classification services has classified and scored nearly100% of the public web, creating one of the industry’s largest URL intelligence databases.
As cyber threats evolved and new websites appeared continuously, the company’s SQL Server-based architecture could no longer keep pace with the speed, scale, and complexity of real-time website classification. Every new website represented a potential threat, requiring accurate classification in milliseconds to help protect customers before malicious activity could spread.
The challenge extended beyond processing larger volumes of data. The company needed to understand the relationships between URLs, domains, websites, threat signals, infrastructure, and historical classifications fast enough to identify emerging threats as they developed. Traditional systems, designed to analyze isolated records rather than connected behavior, could not deliver the real-time relationship intelligence required to operate at internet scale.
As cyber threats evolved and new websites appeared continuously, the company’s SQL Server-based architecture could no longer keep pace with the speed, scale, and complexity of real-time website classification. Every new website represented a potential threat, requiring accurate classification in milliseconds to help protect customers before malicious activity could spread.
The challenge extended beyond processing larger volumes of data. The company needed to understand the relationships between URLs, domains, websites, threat signals, infrastructure, and historical classifications fast enough to identify emerging threats as they developed. Traditional systems, designed to analyze isolated records rather than connected behavior, could not deliver the real-time relationship intelligence required to operate at internet scale.
The Solution
The company implemented TigerGraph to power the next generation of its cloud-based threat intelligence platform with real-time graph analytics and machine learning. TigerGraph unified website classifications, URL risk scores, threat intelligence feeds, infrastructure data, and historical threat patterns into a continuously evolving graph. Rather than evaluating websites in isolation, the company could analyze the relationships between connected entities to uncover suspicious infrastructure, identify emerging threat patterns, and classify previously unseen websites with greater speed and accuracy.
Graph-enriched intelligence was seamlessly integrated into machine learning workflows, allowing the platform to continuously improve as new websites and threat signals emerged. The result was a highly scalable graph-powered architecture capable of delivering real-time threat intelligence while keeping pace with the constantly expanding internet.
Graph-enriched intelligence was seamlessly integrated into machine learning workflows, allowing the platform to continuously improve as new websites and threat signals emerged. The result was a highly scalable graph-powered architecture capable of delivering real-time threat intelligence while keeping pace with the constantly expanding internet.
The Results
The new platform significantly improved the company’s ability to identify and classify emerging cyber threats in real time, helping customers receive faster, more accurate protection against malicious websites.
By combining graph analytics with machine learning, the company now supports thousands of website classifications per second while continuously expanding one of the industry’s largest URL intelligence databases. The platform delivers relationship-aware threat intelligence at internet scale, enabling the organization to respond more quickly to evolving cyber threats while reinforcing its leadership in cloud-based cybersecurity.
By combining graph analytics with machine learning, the company now supports thousands of website classifications per second while continuously expanding one of the industry’s largest URL intelligence databases. The platform delivers relationship-aware threat intelligence at internet scale, enabling the organization to respond more quickly to evolving cyber threats while reinforcing its leadership in cloud-based cybersecurity.
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