As AI applications move from experimentation into production, security teams need more than model-level safeguards. They need a control plane that can enforce policy consistently across prompts, agents, tools, and model endpoints. This session presents the AI gateway as that control plane.

I’ll walk through a practical gateway-based security architecture built around three layers of control for production AI systems:
- Content controls inspect and block risky requests and responses, including prompt injection attempts, unsafe content, and sensitive data exposure.
- Access controls govern which users, apps, agents, and MCP tools can reach model and API endpoints through authentication and authorization policies.
- Operational controls provide the limits, monitoring, and enforcement mechanisms needed to keep AI workloads safe, reliable, and manageable at scale.

Using Azure API Management and Azure Content Safety as the implementation example, this session shows how these controls work together in a real deployment. Let’s discuss how to reduce AI risk, apply stronger enforcement at the gateway layer, and build safer AI applications for production use.