Hello Apigee Community! ![]()
In our latest TechTalk, Apigee South EMEA Manager & Barista, Luis Cuellar explores how to take generative AI workloads from prompt to production. He demonstrates how to leverage Apigee’s declarative workflows to spin up a fully governed LLM gateway in minutes using basic environment variables.
Access the presentation slides
Core architectural pillars and protections ![]()
Deploying an enterprise AI gateway requires robust security and operational control without adding complexity. Luis highlighted four core pillars:
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Rapid LLM gateway deployment: Using declarative configuration variables to set up and verify an Apigee LLM gateway instantly.
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Active model defense & data protection: Layering Model Armor to block prompt injection, jailbreaks, and malicious inputs, alongside Cloud Data Loss Prevention (Cloud DLP) policies to automatically redact sensitive payload data in real time.
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Access control and performance: Enforcing authentication, fine-grained authorization, execution quotas, rate limits, and intelligent response caching to manage cost and latency.
Key takeaways from the session ![]()
Luis provided a technical walkthrough demonstrating how simple it is for developers and platform engineers—regardless of deep machine learning expertise—to configure an end-to-end AI workflow. The demo showed how Apigee intercepts LLM requests, applies real-time prompt protection and data redaction, enforces traffic policies, and safely handles agentic interactions before requests reach backend models.
Leave behind resources ![]()
Next steps ![]()
Continue the conversation: How is your organization handling prompt security and token governance for your AI workflows? Share your architectural ideas or ask Luis your questions in the comments below!
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