Deploying Large Language Models (LLMs) in enterprise environments demands more than just cutting-edge models, it requires robust guardrails to ensure safety, compliance, and ethical AI usage. Without proper safeguards, LLMs can generate harmful content, bypass security constraints, or introduce regulatory risks.

In this session, we’ll explore how to integrate AI safety frameworks into your applications using tools like Granite Guardian, Llama Guard, Safety Checker, IBM Risk Atlas, TrustyAI, and others. We’ll break down how these solutions detect and mitigate risks, ensuring that AI systems remain trustworthy and aligned with enterprise requirements.

Through live demos, we’ll demonstrate how to implement risk detection and response mechanisms that filter harmful prompts before they reach the LLM, prevent unauthorized actions, and maintain compliance with industry standards. We’ll also showcase how to integrate these safeguards within Kubernetes and OpenShift, creating scalable, policy-driven protections that adapt to evolving AI risks.

Attendees will walk away with practical insights on securing AI applications in production, enforcing ethical AI policies, and building trust in AI-driven decision-making.