Generative AI workloads require their own landing zone controls beyond standard cloud governance. Microsoft Fabric, Azure OpenAI, AWS Bedrock and Google Vertex AI all introduce new security, governance and architecture requirements.
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Extends standard cloud landing zone with AI-specific controls: dedicated AI subscriptions/projects, private endpoints for AI services, data governance for training data, responsible AI policies, model registry, cost governance for GPU/inference spend, AI incident response.
Fabric consolidates Power BI, ADF, Synapse, ADLS and real-time analytics. Requirements: dedicated Fabric capacity (F SKU starts at F2 = $0.36/hr), OneLake as single data lake, workspace governance per team, private endpoints for Fabric APIs, Microsoft Purview for data access controls.
Key controls: private endpoint (no public internet), Azure API Management as gateway for rate limiting and logging, managed identity (not API keys) for app auth, diagnostic logging to Log Analytics, content filtering policies, Azure Policy to restrict regions.
VPC endpoints for Bedrock API (no public internet), IAM least privilege for model access, CloudTrail logging for all Bedrock calls, AWS Config rules, data residency controls, Bedrock Guardrails for content filtering.
Token-based pricing for LLMs is unpredictable and can spike dramatically. GPU compute: spot instances save 60-90% for tolerant workloads. Controls: API rate limits per team, cost alerts specific to AI services, FinOps tagging for AI vs non-AI spend separation.
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