LLM-powered applications are moving from demos to production, and that’s where most teams discover their blind spots. From prompt injection and tool misuse to privacy leaks and silent model drift, deploying AI systems introduces risks that traditional cloud architectures were never designed to handle.

In this session, we’ll walk through a practical five-pillar framework for hardening AI systems in real-world environments. Attendees will learn how to design defensive prompts, validate inputs and outputs, enforce safe tool permissions, protect sensitive data, and monitor probabilistic systems after deployment.

This talk focuses on building AI systems that don’t just work in staging, but survive in production.