Productized engagement
Secure AI → SI foundations.
Build the Azure infrastructure and operating controls that AI workloads depend on. This engagement focuses on a governed environment for model, search and application services; it does not presume a particular AI application or model choice.
Who it is for
- CTOs, security and cloud architects preparing an Azure environment for internal or customer-facing AI workloads.
- Platform teams that need approved AI service patterns before multiple application teams deploy independently.
Problems addressed
- AI services are deployed without a consistent subscription, governance or network boundary.
- Public endpoints, unmanaged credentials or unclear data paths complicate security review.
- Logging, access ownership and configuration differ between AI experiments and environments.
Typical scope
- Map the intended Azure AI Foundry, Azure OpenAI and Azure AI Search services and their data flows.
- Design network segmentation, private endpoints, DNS resolution and permitted connectivity paths.
- Define managed identity, Key Vault integration, governance policy and environment access boundaries.
- Establish diagnostic logging and an Infrastructure as Code deployment pattern for agreed components.
Deliverables
- AI landing-zone architecture, service/data-flow diagram and documented security decisions.
- Network, identity, Key Vault, governance and logging configuration for the agreed scope.
- Terraform or Bicep deployment code and validation guidance.
- Operational notes for access ownership, diagnostics and onboarding AI workloads.
Related service: Enterprise AI.