Productized engagement
Secure AI → SI foundations.
Build the cloud 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 cloud environments 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 account or subscription, governance or network boundaries.
- 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 intended model, search and AI application services available from the selected cloud providers, along with 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 provider-native infrastructure code deployment code and validation guidance.
- Operational notes for access ownership, diagnostics and onboarding AI workloads.
Related service: Enterprise AI.
Related case study patterns: secure AI infrastructure and governed Azure foundations.