Azure AI Landing Zone
Build a secure, governed Azure AI Landing Zone.
Agents built without a foundation accumulate architectural debt of things like public endpoints, unowned subscriptions, and security being retrofitted.
We design and deploy a pre-configured environment that encodes identity, networking, security, and compliance guardrails from day one, so AI workloads move from PoC to production without accumulating debt.
The Challenge
AI workloads do not fit cleanly into traditional Azure Landing Zones.
They need isolated compute, private connectivity, quota controls, model access governance, and secure data paths from day one. Agent identity also must be part of the foundation, not bolted on after the first deployment.
Without an AI-ready landing zone, every workload becomes a bespoke security review. Teams re-negotiate the same controls, rebuild the same patterns, and create exceptions instead of reusable platform capability.
Over time, the platform stops being a foundation and becomes a collection of one-off approvals, fragile workarounds, and unmanaged risk.
The Solution
To scale AI safely, the landing zone must become the governed foundation, not an exception path.
What We Deliver
Key Outcomes:
To establish a secure AI foundation, we deliver the core landing zone components needed to scale AI workloads consistently across Azure.
AI Landing Zone Blueprint
A target architecture and landing zone design that separates shared platform services from AI application spokes.
Readiness Assessment
A review across identity, networking, governance, and security, with a readiness scorecard and prioritized remediation plan.
Subscription and Management Design
Dedicated AI workload subscriptions aligned to a management group hierarchy with AI-specific guardrails.
Operational Control Model
A repeatable control model for onboarding, monitoring, and governing AI workloads without creating one-off platform exceptions.
Hub-and-spoke Network Design
Private connectivity for Azure AI Foundry, Azure OpenAI, and storage, with outbound egress controls to reduce exfiltration risk.
Policy and IaC Pack
Azure Policy guardrails delivered as Bicep or Terraform using Azure Verified Modules, with Defender for Cloud and Sentinel integration.
How It Works
We start with a current-state discovery where we assess your environment, identify gaps, and define the next
steps for governance, identity, and architecture.
From there, we design the topology and policy set, then deploy the foundation as infrastructure as code.
The first workload is onboarded against the blueprint, with handover documentation so teams can continue with confidence, expanding iteratively as new AI workloads are onboarded through the same governed pattern
Related Services
FAQ
Azure AI Foundry and APIM access, your Azure Policy and Bicep or Terraform preferences, and Defender for Cloud and Sentinel access. We also nominate owners and run design workshops with your team.
Both. The main estimating factor is the maturity of your existing landing zone. We assess greenfield versus brownfield up front and size the work accordingly.
It is the proven enforcement point for auth, quota, and observability across Azure, and it fronts agent and MCP traffic too.
No. We separate platform services (hub) from AI application spokes and integrate with your existing subscriptions
Talk to Us
Get in touch if you want to discuss your challenges or questions.
Harri Jaakkonen
Principal Security Engineer
oi.owtytrofobfsctd-908493@nenokkaaj.irrah
Every agent leaves a trail and it needs to be auditable.
