Onevinn AI Citadel - AI Landing Zone & Governance Hub

Many organizations start AI initiatives quickly but struggle to move them into production because governance, identity, networking, observability, compliance, and deployment automation are fragmented across teams. Manual Azure portal configuration increases risk, slows onboarding, and makes it difficult to apply consistent controls across business units and regulated workloads. Onevinn helps platform owners, enterprise architects, security leaders, and engineering teams deploy an AI-ready Azure Landing Zone foundation aligned to the Microsoft Cloud Adoption Framework for Azure and the Azure Well-Architected Framework. The engagement combines proven architecture patterns, automated Infrastructure as Code, validation scripts, and enablement so your teams can onboard AI workloads faster while maintaining central governance from day one. The service delivers a Onevinn AI Citadel Governance Hub and reusable Agent Spoke landing zones. The Governance Hub provides centralized runtime control, model and API access patterns, monitoring, policy enforcement, and operational oversight. Agent Spokes provide governed application landing zone patterns for AI agents, AI applications, APIs, microservices, and supporting enterprise services.

What You Receive:

Onevinn AI Citadel Governance Hub - A centralized Azure foundation for AI governance and runtime control, including:

Management group, subscription, and resource organization guidance.

• Azure Policy guardrails and tagging standards.
• Azure role-based access control model aligned to Microsoft Entra ID.
• Azure API Management patterns for governed model, API, and agent access.
• Azure AI Foundry integration options for model and project governance.
• Centralized logging and telemetry with Azure Monitor and Log Analytics.
• Cost management baseline and chargeback or showback guidance.
• Automated deployment using Infrastructure as Code and repeatable pipelines.
• Deployment validation scripts and operational runbooks.
• Onevinn AI Citadel Agent Spoke Landing Zone
• Reusable application landing zone patterns for AI workloads, including:

AI Foundry project and service patterns for agent and application teams.

• Network topology options, including peered hub-spoke or bring-your-own VNet.
• Private endpoint, managed identity, Key Vault, and secure configuration patterns.
• AI Search, Storage, Cosmos DB, Container Apps, and container registry options for common AI application architectures.
• Predefined sizing presets for development, team production, department, and enterprise-scale deployments.
• Deployment validation scripts for networking, RBAC, telemetry, and topology correctness.
• Onboarding guidance for additional teams and workloads.
• Regulated and Sovereign-Ready Variant

For organizations with heightened data residency, public sector, or regulated industry requirements, Onevinn can extend the implementation with:

• Region pinning and workload placement guidance.
• Restricted connectivity and private networking patterns.
• Encryption, logging, auditing, and evidence collection guidance.
• Policy enforcement for data handling and operational control.
• Compliance-oriented architecture diagrams and operational runbooks.

Expected Outcomes

• Production-ready Azure foundation for governed AI workloads.
• Repeatable Governance Hub and Agent Spoke landing zone deployment patterns.
• Consistent identity, networking, observability, policy, and security baseline.
• Reduced manual Azure portal configuration through automated deployment.
• Faster onboarding of AI applications, agents, APIs, and enterprise workloads.
• Clear operating model supported by documentation, runbooks, and validation.
• Improved readiness for compliance, audit, chargeback, and future scale.

Customer Challenges Addressed

• Fragmented AI governance across teams and subscriptions.
• Inconsistent identity, networking, policy, and monitoring configuration.
• Manual deployment processes and hard-to-repeat portal changes.
• Slow workload onboarding due to unclear platform patterns.
• Limited visibility into AI usage, cost, telemetry, and operational posture.
• Difficulty applying governance consistently across AI agents and applications.