Enterprise AI Architecture

Introduction

Enterprise AI architecture combines secure data layers, governance, identity, audit, multi-tenancy, observability and integrations — wrapped around the AI workflow.

Beginner analogy: Like a normal enterprise architecture diagram, with an AI layer added that connects to LLMs, vector DBs and evals.

Understanding the topic

Core concepts to understand:

• Identity: SSO (Okta, Azure AD), SCIM provisioning.
• Data: encrypted at rest, residency aware, ACL-enforced retrieval.
• Models: multi-provider gateway, on-prem option for regulated.
• Observability: traces, metrics, audit logs.
• Governance: prompt review, data classification, red-team tests.

Syntax reference

Visual workflow / architecture:

Users ─► SSO ─► API Gateway ─► AI Workflow
                                    │
                          ┌─────────┼─────────┐
                          ▼         ▼         ▼
                     Vector DB   LLM GW   Connectors
                          │         │         │
                          └────► Audit · Logs ◄┘

Real-world use

Glean, Sierra, Cresta, Microsoft Copilot for Microsoft 365 all ship enterprise architectures like this.

Best practices

• Bake in audit logs from day one.
• Prefer regional data residency.
• Run quarterly red-team / pen-test.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Sketch an enterprise AI architecture.
• Q2. What governance is required?
• Q3. How do you support data residency?