Securing API Traffic: PII Masking, Prompt Guardrails, & Data Sovereignty

As organizations move from experimenting with LLMs to running AI workloads in production, managing AI traffic becomes increasingly complex. Applications may depend on multiple model providers, while organizations need to control access, protect sensitive data, manage costs, and maintain visibility across AI usage. An AI Gateway provides a centralized way to govern these interactions without tightly coupling applications to individual LLM providers.

In this hands-on lab, you will work with WSO2 AI Gateway to explore how it can be used to secure and govern AI traffic. You will work through practical scenarios including routing requests across multiple LLM providers, implementing failover strategies, and securely exposing models to applications without distributing provider credentials.

You will also configure AI-specific security and governance controls, including prompt validation, PII protection, content filtering, and token-based rate limiting. The lab will demonstrate how analytics can provide visibility into model usage, token consumption, and costs, helping teams understand and manage their AI workloads.

Finally, we will explore how the AI Gateway can be deployed close to your applications and data. You will see how a distributed data-plane architecture can keep AI traffic and sensitive observability data within your own infrastructure while still providing centralized management and governance.

By the end of the lab, you will have hands-on experience building a secure, governed, and multi-provider AI gateway architecture that can be applied to real-world enterprise AI workloads.