Secure AI Landing Zone Blueprint
A cloud reference structure for hosting enterprise AI safely on Azure, AWS, and GCP with governance and observability built in.
What the asset is
A blueprint covering IAM/RBAC, network isolation, data classification, AI service access, secrets handling, logging, monitoring, Sentinel/GuardDuty/Security Command Center patterns, audit trails, and cost controls.
Who it is for
Cloud leaders, CISOs, platform teams, data teams, and CTOs who need a secure foundation before AI pilots scale.
Business problem supported
AI pilots often start outside governance. This blueprint shows how to create a controlled landing zone before risk expands.
What the buyer learns
How Azure OpenAI, AWS Bedrock, Vertex AI, Kubernetes, monitoring, and security controls fit into a governed AI platform.
How DigiScience uses it
It supports platform discovery, proposal architecture, security review, and the technical baseline for AI pilots.
Cloud cost governance for AI workloads
Cloud cost optimisation should start with evidence, not a generic savings target. This planning checklist applies to Azure, AWS, and GCP when a team is preparing AI, data, or platform workloads.
Allocate and explain spend
Tag environments, products, teams, and experiments so finance and delivery leaders can trace material cost to a workload and owner.
Set budgets and guardrails
Define budget thresholds, alerts, usage limits, and approval points before deploying GPU, model API, data transfer, or always-on services.
Measure unit economics
Track cost per workflow, document, transaction, user action, or model evaluation—not only total cloud spend.
Choose the right placement
Compare managed services, private deployment, hybrid options, schedules, capacity commitments, and model choices against security, latency, resilience, and cost requirements.
This is not a claimed saving or a financial guarantee. It identifies what must be measured before a cost optimisation decision is credible.