Build the Azure Foundation for Production AI
Prepare your Azure environment for AI applications and agent workloads with the right infrastructure, identity, security, and governance in place.
- Azure Landing Zone
- Identity & Permissions
- Infrastructure as Code & Handoff
DISCUSS YOUR AZURE ENVIRONMENT
Moving AI From Pilot to Production?
AI pilots can move quickly. Production brings additional questions around infrastructure, access, security, governance, and ongoing ownership.
Azure Foundation
Your AI workloads need a consistent Azure foundation for networking, security, governance, monitoring, and resource management. We help teams build or extend that foundation before workloads move into production.
Identity
AI agents and applications need a clear identity model. We help teams determine how non-human workloads should authenticate and access Azure and Microsoft services without relying on unnecessary user credentials.
Permissions
AI applications may need access to Microsoft 365 data through Microsoft Graph. We help define permissions around what the workload actually needs and reduce unnecessary access wherever possible.
Operations
Production AI needs more than a working deployment. Internal IT should be able to understand, monitor, maintain, and govern the environment after implementation.
The Foundation Behind Production AI
Horizons helps internal IT teams put the core Azure infrastructure in place for AI and agent workloads.
Azure Landing Zones
Design or extend Platform Landing Zones and Application / Workload Landing Zones around your existing Azure environment. The goal is to create a consistent foundation for networking, governance, security, monitoring, and workload deployment.
Identity for AI Workloads
Choose an appropriate identity model for AI agents, applications, and other non-human workloads. This may include managed identities or other workload identity approaches based on how the application needs to authenticate.
Microsoft Graph Permissions
Define the Microsoft Graph access an AI application or agent actually requires. Permissions can then be scoped around the workload instead of granting broader access than necessary.
Exchange RBAC
Where AI workloads need Exchange Online data, additional access controls may be required. Exchange RBAC can help restrict access to the mailboxes or resources relevant to the use case.
Security & Governance
Apply consistent security, policy, logging, monitoring, and governance controls across the environment. This gives internal IT better visibility and a clearer operational model as AI workloads scale.
Infrastructure as Code
Use version-controlled Infrastructure as Code, such as Bicep or Terraform where appropriate. This helps make Azure environments more consistent, repeatable, and easier for internal teams to manage.
Built With Internal IT. Handed Back to Internal IT.
Horizons works alongside your IT team rather than building an environment that only an outside consultant understands.
Review
We review the existing Azure environment, planned AI workload, identity requirements, access needs, and operational model.
Build
We work with your team to implement the required landing zone, identity, permissions, security, governance, and deployment foundation.
Hand Off
Your team receives the architecture documentation, Infrastructure as Code, and implementation guidance needed to operate the environment going forward.
Microsoft-Focused Infrastructure Expertise
Horizons helps organizations build and modernize Microsoft cloud infrastructure across Azure, identity, Microsoft 365 security, and governance.
As a Microsoft Solutions Partner, we focus on building practical infrastructure that internal IT teams can understand and own.
Planning an AI or Agent Workload in Azure?
If your team is working through Azure architecture, workload identity, Microsoft Graph permissions, governance, or production readiness, let’s discuss the infrastructure behind it.