This guide explains how to secure generative AI workloads in Azure by combining cloud and AI-specific controls. It stresses strong authentication using managed identities instead of static API keys, enforcing least-privilege RBAC, and protecting data with encryption and customer-managed keys. Network isolation through private endpoints and filtering is key to limiting exposure. It also highlights AI-specific risks like prompt injection and recommends red teaming, content filtering, and runtime guardrails. Governance practices such as responsible AI frameworks and compliance monitoring are also essential. The takeaway is that GenAI security requires layered defenses spanning infrastructure, data, and model behavior.

Join for free to read