In the realm of artificial intelligence (AI), especially when applied to networking and security, the accuracy and reliability of data are paramount. AI systems are only as good as the data they consume, and for network engineers, feeding these systems with up-to-date and precise information can make the difference between optimal performance and costly errors.

In the rapidly evolving cybersecurity landscape, the sheer volume of Common Vulnerability and Exposure (CVE) notices has become a daunting challenge for SecOps teams. In 2023 alone, the National Institute of Standards and Technology (NIST) issued 28,901 CVE notices, reflecting the growing complexity and intensity of potential threats.

In today's rapidly evolving IT landscape, effective change management is crucial for maintaining network stability and performance, but many organizations lack a consistent change process. Lack of process often leads to unintended consequences, such as outages and performance degradation. A network digital twin offers a transformative solution to these challenges by streamlining change management processes and providing pre- and post-change verification to enhance operational efficiency.

In today’s complex IT environments, maintaining full network visibility is a daunting task. Network segmentation, while essential for security, often complicates the process of obtaining a comprehensive view of the entire network infrastructure. Federal IT departments charged with the responsibility of keeping networks mission-ready, face significant hurdles in visualizing connectivity, ensuring security zone segmentation, and performing complete path searches across segmented domains.

Natural language prompts put the power of NQE into the hands of every networking engineer As featured in Network World, Forward Networks has raised the bar for network digital twin technology with AI Assist. This groundbreaking addition empowers NetOps, SecOps, and CloudOps professionals to harness the comprehensive insights of NQE through natural language prompts to quickly resolve complex network issues. See the feature in action.

Digital twins are often associated with manufacturing, where a virtual replica mimics the workings of a complex physical system, such as a jet engine or a machine on a production line. But increasingly, there is interest from enterprises, telecom companies, and cloud providers in applying the technology to networks.