An AI agent that can call tools and take actions is not a chatbot. It is a new identity and a new attack surface. Here are the guardrails I would put around agentic workloads on Azure OpenAI and AI Foundry, built from the same principle I apply everywhere: enforce at the point every request passes through.
Generative AI on Azure introduces control points that traditional application security never had to handle. Here are practical governance patterns for Azure OpenAI and AI Foundry workloads, mapped to where the real risks live.
Most AI security programs pass the pilot and break in production. The reason is structural, not a tooling gap. Here are the seven failure modes scale exposes, and why governance, not more tools, is the fix.
Frameworks tell you what outcomes to achieve. They do not tell you how to instrument your system to produce them. That gap, between risk models and real enforcement, is where most AI governance programs are stuck.
Testing samples an AI system’s behavior at one moment. Trust requires governing that behavior continuously. Here is the trust triad, governance, validation, and monitoring, and why conflating the three is the mistake that breaks at scale.
In cloud-native and AI-driven systems, compliance can no longer be a periodic activity. It has to be continuously demonstrated. Here is the architecture for engineering that trust.