The 5 Security Layers Every AI System Needs Before Production скачать в хорошем качестве

The 5 Security Layers Every AI System Needs Before Production

In this video, I walk through the 5 security layers every AI system needs before it goes to production, using a real Azure RAG architecture as the example. Whether you are building on Azure, AWS, or any cloud platform, these principles apply to any AI workload you are shipping. What is covered in the video: Network Isolation — removing public access and routing traffic through private endpoints DNS Governance — ensuring your services always resolve to private IPs, never public ones Identity Segmentation — scoping permissions with managed identities so each component only does what it needs to do Traffic Containment — using firewall rules and zero trust principles to limit blast radius if something goes wrong Observability and Enforcement — monitoring, alerting, and policy enforcement so you catch drift before it becomes a breach This is not theory. This is the architecture review that most teams skip because the pilot worked and they moved fast. By the end of this video you will know exactly what questions to ask and what to build before you ship any AI system to production. If you are working with Azure OpenAI or any other model, Azure AI Search, or any retrieval augmented generation system, this video is for you. Subscribe for weekly videos on Azure architecture, AI infrastructure, and cloud security for developers and engineers building real systems.