Objective: Design and implement an enterprise-grade centralized AI platform that provides secure, compliant, and ethical AI services across the business.

Key technologies and scope:

• Azure: Container Apps, PostgreSQL, Blob Storage
• AI/LLM Gateway: LiteLLM, model routing, rate limiting
• Authentication: OAuth 2.1, Microsoft Entra ID, SSO
• Integrations: MCP (Model Context Protocol) to connect with Atlassian (Jira, Confluence)
• Governance: ISO 27001:2022 alignment, audit logging, access controls
• DevOps/IaC: Docker, CI/CD, Azure CLI

Expected deliverables:

• Extend the existing Open WebUI deployment on Azure
• Team-dedicated AI workspaces with isolated knowledge bases
• Enhanced OAuth 2.1 authentication flows and comprehensive audit logging
• Documentation and training materials to drive organizational AI adoption
• A governed, centralized platform to eliminate shadow AI

Learning outcomes:

• Understand enterprise AI governance frameworks and alignment with information security standards
• Design secure AI service architectures with strong access control and auditing
• Deploy containerized AI apps on Azure
• Implement MCP integrations for enterprise tools (Jira, Confluence)
• Produce end-user documentation and training for AI adoption