Ingress Security for AI Workloads in Kubernetes: Protecting AI Endpoints with WAF

AI Workloads Have a New Front Door Why the Stakes Have Changed for Platform Teams Why AI Inference Changes the Ingress Security Model The High Cost of the “Successful” Request AI-Specific Ingress Threats Platform Teams Are Seeing Resource Exhaustion and LLM Jacking Prompt Injection & Input Abuse Data Exposure and the AI Pipeline The Ingress Blind Spot in Kubernetes Today Securing AI Ingress with Calico Ingress Gateway Integrated Web Application Firewall Identity-Aware Access Control Fine-Grained Rate Limiting for AI Workloads Real-Time Observability and Security as Code Security as an AI Enabler Secure Your AI Infrastructure Today Scale AI Safely with Calico For years, AI and machine learning workloads lived in the lab. They ran as internal experiments, batch jobs in isolated clusters, or offline data pipelines. Security focused on internal access controls and protecting the data perimeter. That model no longer holds. Today, AI models are increasingly part of production traffic, which is driving new challenges around securing AI workloads in Kubernetes. Whether serving a large language model for a customer-facing chatbot or a computer vision model for real-time analysis, these models are exposed through APIs, typically REST or gRPC, running as microservices in Kubernetes. From a platform engineering perspective, these AI inference endpoints are now Tier 1 services. They sit alongside login APIs and payment gateways in terms of criticality, but they introduce a different and more expensive risk profile than traditional web applications. For AI inference endpoints, ingress security increasingly means Layer 7 inspection and WAF (Web Application Firewall) level controls at the cluster edge. By analyzing the full request payload, a WAF can detect and block abusive or malicious traffic before it ever reaches expensive GPU resources or sensitive data. This sets the stage for protecting AI workloads from both operational and security risks. Exposing AI workloads to the internet creates a direct path to some of your most sensitive and costly infrastructure.