Infrastructure for AI-enablement for a large NGO Project

This document outlines the infrastructure architecture and deployment process for the AI-enablement for a large NGO project, focusing on how the system was designed and deployed to enable AI capabilities.

Infrastructure Overview

The AI-enablement for a large NGO project is built on Microsoft Azure, leveraging a variety of Azure services to create a scalable, secure, and AI-enabled application. The infrastructure is defined and deployed using Infrastructure as Code (IaC) with Terraform and automated through Azure DevOps pipelines.

Core Infrastructure Components

1. App Services

   • Multiple App Services for different use cases (UC1, UC2, UC3, UC4)
   • Linux-based web applications running Python
   • Integrated with Azure AD for authentication
   • Connected to private virtual networks for enhanced security

2. Azure Functions

   • Serverless compute for background processing and API endpoints
   • Integrated with storage and database services
   • Python runtime environment

3. Azure Cosmos DB

   • NoSQL database for storing application data
   • Separate databases and containers for different use cases
   • Configured with appropriate throughput and partition keys

4. Azure Storage

   • Blob storage containers for each use case
   • Used for storing documents, files, and other unstructured data

5. Application Insights

   • Monitoring and telemetry for all application components
   • Performance tracking and error logging

6. Virtual Network Integration

   • Private endpoints for secure service communication
   • Subnet integration for App Services and Functions
   • Network isolation for enhanced security

AI Integration Architecture

The project leverages Azure OpenAI services for AI capabilities, with the following integration points:

Azure OpenAI Service Integration

1. Configuration

   • OpenAI endpoints and API keys are securely stored and passed to applications
   • API version management for compatibility
   • Secret management for API keys

2. App Service Integration

   • Each App Service is configured with the necessary OpenAI connection parameters:
     • AZURE_OPENAI_ENDPOINT
     • AZURE_OPENAI_API_KEY
     • OPENAI_API_VERSION

3. Function App Integration

   • Function Apps are configured with OpenAI connection settings:
     • AZURE_OPENAI_ENDPOINT
     • AZURE_OPENAI_KEY

4. Security Considerations

   • API keys are stored as secure application settings
   • Virtual network integration provides network-level isolation
   • System-assigned managed identities for secure service-to-service authentication

Infrastructure as Code (IaC)

The entire infrastructure is defined using Terraform, with a modular approach:

Module Structure

• App Service Plan Module: Defines the hosting plan for web applications
• App Service Module: Configures web applications with AI integration
• Function App Module: Sets up serverless functions with AI capabilities
• Cosmos DB Module: Configures the NoSQL database
• Storage Account Module: Sets up blob storage

Environment Configuration

The infrastructure supports multiple environments:

• Production (prod)
• User Acceptance Testing (uat)
• Development (shared-dev)
• Demo (demo)

Each environment has its own configuration files with appropriate variable values.

Deployment Process

The infrastructure is deployed using Azure DevOps pipelines with a structured approach:

CI/CD Pipeline

The azure-pipeline.yaml defines a multi-stage deployment process:

1. Generate TFVars Stage

   • Creates environment-specific variable files
   • Publishes variables as pipeline artifacts

2. Terraform Plan Stage

   • Initializes Terraform with backend configuration
   • Creates an execution plan
   • Publishes the plan as an artifact

3. Terraform Apply Stage

   • Applies the approved Terraform plan
   • Requires environment approval for production deployments
   • Uses service principal authentication

4. Terraform Destroy Stage (conditional)

   • Provides the ability to tear down infrastructure when needed
   • Requires explicit approval

Security in the Deployment Process

• Service Principal authentication for Azure resources
• Pipeline variables for sensitive information
• Environment approvals for production deployments
• Secure storage of state files in Azure Storage

AI Enablement Features

The infrastructure supports several AI capabilities:

1. Azure OpenAI Integration

   • Direct connection to Azure OpenAI services
   • API version management for compatibility
   • Secure key management

2. Data Storage for AI Processing

   • Cosmos DB containers for storing AI-processed data
   • Blob storage for document analysis and generation

3. Scalable Compute

   • App Service Plans sized appropriately for AI workloads
   • Function Apps for asynchronous AI processing

4. Monitoring and Observability

   • Application Insights integration for tracking AI performance
   • Logging of AI operations for auditing and improvement

Network Security

The infrastructure implements several security measures:

1. Virtual Network Integration

   • App Services and Functions integrated with VNets
   • Private endpoints for database and storage access

2. Access Controls

   • Azure AD integration for authentication
   • Security groups for authorization
   • Role-based access control

3. Data Protection

   • HTTPS enforcement
   • Secure storage of secrets and keys

Conclusion

The AI-enablement for a large NGO project infrastructure is designed with a focus on security, scalability, and AI enablement. By leveraging Azure’s PaaS offerings and implementing Infrastructure as Code practices, the project achieves a robust foundation for AI-powered applications with automated deployment processes.

The modular approach to infrastructure definition allows for easy maintenance and extension, while the multi-environment support enables a proper development lifecycle from development to production.