An Interactive Guide to Building AI on Azure
This application translates the definitive blueprint for building enterprise-grade AI in financial services into an actionable, explorable journey. Navigate through the strategic phases to understand how to build a secure, compliant, and scalable platform on Microsoft Azure, from foundational setup to advanced autonomous systems.
Phase 1: Establish the Compliant Core
The journey into AI must begin with a secure and compliant foundation. This phase is about building the mandatory, auditable "runway" for all future AI initiatives, ensuring regulatory demands are met by design, not as an afterthought.
The FSI Landing Zone Architecture
The Azure Financial Services Industry (FSI) Landing Zone is a prescriptive, secure-by-default environment. It codifies compliance into the infrastructure itself. Explore the key components below.
Key Foundational Pillars
The FSI Landing Zone is built on several core principles that ensure a robust and governable cloud estate.
-
➔
Governed Agility: Empowers development teams with autonomy inside pre-secured subscriptions, balancing innovation speed with central control.
-
➔
Segregation of Duties: Uses a specific management group hierarchy and dedicated subscriptions (Management, Connectivity, Identity) to enforce clear operational boundaries.
-
➔
Policy-Driven Governance: Leverages Azure Policy to automate compliance, ensuring the environment cannot drift from its secure state.
Phase 2: Pilot & Learn
With the foundation in place, the next step is to demonstrate value through a controlled pilot project. This phase focuses on building a governed data estate and exploring foundational AI capabilities like Generative AI.
Building the Governed Data Estate
AI is fueled by data. This architecture transforms siloed data into a trusted, centralized asset using key Azure services.
Azure Data Lake
Central repository for all data (structured/unstructured).
Data Factory & Synapse Link
Orchestrates data ingestion from any source into the lake.
Microsoft Purview
Provides unified governance, auto-classifying data and tracking lineage.
Core AI Services
Azure provides a suite of services to build powerful AI applications, all unified within the Azure AI Foundry.
- Azure OpenAI: Secure access to models like GPT-4.
- Azure AI Search: Powers information retrieval for RAG.
- Azure AI Services: Pre-built intelligence (e.g., Document Intelligence).
- Azure Machine Learning: End-to-end custom model development.
GenAI Strategy: RAG vs. Fine-Tuning
A critical decision is how to adapt LLMs with your private data. Retrieval Augmented Generation (RAG) is the recommended starting point for most financial use cases due to its cost-effectiveness and traceability.
Phase 3: Scale & Operationalize
Moving from a successful pilot to an enterprise capability requires industrializing AI development. This phase is about implementing MLOps and LLMOps to make AI repeatable, reliable, and auditable at scale.
The MLOps/LLMOps Lifecycle
This automated lifecycle ensures quality and governance, transforming AI from a "science project" into a managed software asset. Click each stage to see the key Azure tools involved.
Continuous Integration
Continuous Delivery
Continuous Training
Continuous Monitoring
Phase 4: Innovate with Autonomous Automation
With a mature platform, you can explore the next frontier: agentic AI. These are autonomous systems that can plan and execute actions to achieve a goal, shifting the paradigm from decision support to end-to-end process automation.
Reference Architecture for Agentic AI
A production-ready agentic system integrates several key services to provide reasoning, orchestration, memory, and a secure interface to enterprise systems.
Reasoning Engine
Azure OpenAI (GPT-4o) provides the core intelligence for planning and tool use.
Orchestration
Azure AI Agent Service & Azure Functions manage the agent's lifecycle and execute tools.
State & Memory
Azure Cosmos DB provides a low-latency database for storing conversation history and task state.
Secure Gateway
Azure API Management exposes internal systems as secure, governable tools for the agent to use.
Overarching Theme: Zero Trust Security
Security is the non-negotiable prerequisite. A Zero Trust model, which assumes no user or service is inherently secure, must be applied. This architecture maps specific Azure controls to key financial regulations.
Interactive Regulatory Control Mapper
Select a regulatory requirement to see the corresponding Azure controls and services used to address it.
Overarching Theme: FinOps Framework
Long-term viability depends on disciplined financial management. FinOps brings financial accountability to the cloud, enabling data-driven trade-offs between cost, performance, and value.
Optimizing AI Workload Costs
The right pricing model depends on the workload's usage pattern. Select a workload below to see the recommended Azure pricing model and rationale.