Secure Microsoft AI Solutions in the Cloud — Lesson 1: Azure AI Security Architecture and Threat Modeling

Learning Objectives

• Identify AI solution components, data flows, identities, endpoints, models, and dependencies
• Apply shared responsibility, zero-trust, defense-in-depth, and threat-modeling concepts
• Define security, privacy, compliance, availability, and responsible-AI requirements

Guided Demonstration

1. Review the requirement, environment, dependencies, roles, risks, and expected result.
2. Demonstrate the relevant configuration, development, analysis, migration, security, or support workflow.
3. Capture settings, outputs, logs, screenshots, code, reports, or test evidence.
4. Compare the observed result with the acceptance criteria and identify gaps.

Hands-On Activity

Create a threat model and security architecture for an Azure AI solution.

• Document assumptions and the planned sequence before making changes.
• Complete the activity and preserve evidence of normal and exception behavior.
• Record issues, corrective actions, and reusable operational or development notes.

Scenario Analysis

• Identify business, user, data, security, compliance, and operational impacts.
• Compare alternative decisions and explain the selected approach.
• Define ownership, communication, rollback, support, and improvement actions.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, permissions, data, dependencies, capacity, connectivity, and recent changes.
• Validate service restoration, user experience, and prevention measures.

Validation Checklist

• The requirement and expected result are clear.
• The selected configuration, design, code, query, migration, or process is documented.
• Normal, boundary, failure, security, compliance, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Azure AI Security Architecture and Threat Modeling in a realistic scenario and support the result with documented evidence.