Google Cloud and NVIDIA Accelerate Real-Time Threat Detection for Leading Cybersecurity Provider

Palo Alto Networks is a leading global cybersecurity company protecting organizations against advanced threats in the era of generative AI. The company's comprehensive platform integrates advanced firewalls with cloud-based solutions for real-time threat detection and mitigation. However, as cybercriminals leverage AI to create realistic attacks at scale, detecting and responding to threats with minimal latency became critical. Any delay between threat detection and response can allow attacks to succeed. Previously, Palo Alto Networks operated a hybrid infrastructure using on-premises servers and a third-party cloud provider, but as AI data traffic volumes increased dramatically, this architecture became expensive and complex to scale while maintaining the sub-millisecond inference speeds required for effective data loss prevention.

Palo Alto Networks migrated to Google Cloud and deployed NVIDIA GPUs with NVIDIA Triton Inference Server to dramatically reduce inference latency. The combination of Google Cloud Compute Engine's NVIDIA GPU instances and the NVIDIA AI Enterprise platform enabled the deployment of machine learning models that analyze network traffic, user behavior, and data sources in near real-time. This architecture allows the company's AI models to detect and respond to malicious activities before cybercriminals can execute attacks. Palo Alto Networks also leveraged Google Cloud's complementary services—including Vertex AI for model development, BigQuery for data versioning and analysis, and Google Kubernetes Engine (GKE) for container orchestration—to build an integrated, fully managed solution that reduced operational complexity.

The migration delivered immediate results: machine learning inference latency decreased significantly, enabling real-time threat detection and response across distributed networks. The solution proved more cost-effective than the previous cloud provider, thanks to optimized pricing and efficient resource allocation. Google Cloud's flexible GPU options allowed Palo Alto Networks to customize infrastructure for specific workloads, and the platform's intuitive management capabilities streamlined workflows and accelerated troubleshooting. The company can now scale capacity dynamically to meet peak processing demands without overprovisioning. Based on this success, Palo Alto Networks plans to migrate all remaining workloads to Google Cloud, including production GKE deployments, to further improve scalability, resource availability, and centralized management as it continues to tackle evolving cybersecurity threats.