AI Cloud Landing Zone Guide
What a secure, observable AWS or Azure landing zone looks like before the first AI workload ships — networking, IAM, KMS, model endpoints, and CI/CD.
Account structure
Separate accounts/subscriptions for prod, non-prod, shared services, and audit. AI workloads get isolation by default and you can attribute cost without contortions.
Private model endpoints
Bedrock on AWS, Azure OpenAI on Azure, Vertex on GCP — deployed into private subnets with VPC endpoints. No model data leaves your network perimeter.
KMS and secrets
Customer-managed keys for data at rest and for model context where supported. Vendor API keys (OpenAI, Anthropic) go in secret managers with rotation policies, not env vars.
Observability baseline
Traces, metrics, logs wired before the first workload. CloudWatch / App Insights / Datadog + Langfuse or LangSmith for AI-specific traces. Budgets and anomaly alarms.
CI/CD for prompts and models
Prompts, evals, and model configs live in the repo and ship through pipelines like any other code. Rollback is a single commit, not a support ticket.
Compliance scaffolding
HIPAA, SOC 2, and GDPR controls baked in where applicable: audit logs, access reviews, DLP, data residency. The compliance team signs off on the final posture; we give them a posture worth signing off on.
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