This post is a part of the AI-103: Develop AI Apps and Agents on Azure Exam Prep Hub. 
This topic falls under these sections:
Plan and manage an Azure AI solution (25–30%)
   --> Manage, monitor, and secure AI systems
      --> Configure security, including managed identity, private networking, keyless credentials, and role policies

Note that there are 10 practice questions (with answers and explanations) at the end of each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available from the hub's main page below the exam topics section.

Introduction

Security is one of the most important aspects of enterprise AI solutions.

AI applications often process:

Sensitive enterprise data
Proprietary documents
Customer information
Internal business knowledge
Regulated data

Modern AI systems may also:

Access external services
Execute tools
Use vector databases
Retrieve enterprise documents
Orchestrate AI agents

Because of this, organizations must secure:

AI models
APIs
Search services
Data sources
Agent workflows
Networking
Credentials
Access policies

The AI-103: Develop AI Apps and Agents on Azure certification exam tests your understanding of AI security and governance on Azure.

For the AI-103 exam, you should understand:

Managed identities
Keyless authentication
Private networking
Role-Based Access Control (RBAC)
Role policies
Secure service access
Azure networking concepts
Authentication and authorization
Azure Key Vault
Network isolation
Secure AI architectures
Governance and compliance

Why AI Security Matters

AI systems introduce unique security risks.

Examples include:

Data leakage
Prompt injection attacks
Unauthorized tool execution
Credential exposure
Sensitive document access
API abuse
Model misuse

Security controls help:

Protect enterprise data
Enforce least privilege access
Reduce attack surfaces
Improve compliance
Secure AI workflows

Core Azure Security Concepts

Important Azure security concepts include:

Authentication
Authorization
Identity management
Network security
Secrets management
Access control
Governance

Authentication vs Authorization

Authentication verifies identity.

Examples:

User login
Service identity verification

Authorization determines permissions.

Examples:

Which resources users can access
What actions services can perform

Azure Entra ID

Azure Entra ID provides:

Identity management
Authentication
Access control
Enterprise security integration

Azure Entra ID is heavily used in Azure AI solutions.

Managed Identities

Managed identities provide secure identity management for Azure resources.

Managed identities eliminate the need to store credentials in code.

This is an extremely important AI-103 exam topic.

Why Managed Identities Matter

Without managed identities, developers may store:

API keys
Passwords
Secrets
Connection strings

This increases security risks.

Managed identities reduce these risks.

Types of Managed Identities

There are two main types:

System-assigned managed identities
User-assigned managed identities

System-Assigned Managed Identities

A system-assigned identity:

Is tied to one Azure resource
Is automatically managed by Azure
Is deleted when the resource is deleted

User-Assigned Managed Identities

A user-assigned identity:

Exists independently of resources
Can be shared across multiple services
Supports centralized identity management

Common Managed Identity Scenarios

Managed identities are commonly used when:

AI apps access Azure AI Search
AI agents access Blob Storage
Applications access Azure Key Vault
Services call Azure OpenAI

Keyless Credentials

Keyless authentication avoids hardcoded secrets.

Instead of API keys, systems use:

Managed identities
OAuth tokens
Azure Entra authentication

Benefits of Keyless Authentication

Benefits include:

Improved security
Reduced secret management
Automatic credential rotation
Lower risk of credential leaks

Azure Key Vault

Azure Key Vault securely stores:

Secrets
Keys
Certificates
Tokens

Using Key Vault with AI Solutions

AI applications commonly store:

API keys
Database credentials
Connection strings
Encryption keys

inside Key Vault.

Role-Based Access Control (RBAC)

RBAC controls who can access Azure resources.

RBAC uses:

Roles
Permissions
Scope assignments

Principle of Least Privilege

Least privilege means users and services receive only the permissions they need.

This reduces:

Security risks
Accidental misuse
Attack exposure

Common Azure Roles

Common built-in roles include:

Owner
Contributor
Reader
Cognitive Services User
Search Service Contributor

Custom Roles

Organizations may create custom roles with:

Specific permissions
Restricted access scopes

Scope Levels in RBAC

RBAC may apply at:

Management group level
Subscription level
Resource group level
Resource level

AI Role Policy Examples

Examples include:

Developers can deploy models
Analysts can query AI systems
Applications can access search indexes
Agents can retrieve documents

Network Security for AI Systems

AI systems often require secure networking.

Network security helps:

Prevent unauthorized access
Isolate resources
Protect sensitive data

Private Networking

Private networking isolates resources from the public internet.

This is heavily emphasized on AI-103.

Virtual Networks (VNets)

Azure Virtual Networks provide:

Network isolation
Secure communication
Controlled connectivity

Private Endpoints

Private endpoints allow services to be accessed privately through a VNet.

Benefits include:

Reduced internet exposure
Improved security
Private connectivity

Public vs Private Access

Public access:

Uses public internet endpoints
Easier to configure
Higher exposure risk

Private access:

Uses private network paths
Improves security
Supports enterprise compliance

Network Security Groups (NSGs)

NSGs control inbound and outbound traffic.

They support:

Traffic filtering
Security rules
Access restrictions

Firewalls

Azure Firewall helps secure:

Network traffic
Application traffic
Outbound internet access

Secure AI Architecture Example

An enterprise AI system may include:

Azure OpenAI Service
Azure AI Search
Blob Storage
Azure Key Vault
AI agents
VNets
Private endpoints

All connected through private networking.

Secure Agent-Based Systems

AI agents require additional security considerations.

Agents may:

Execute tools
Access APIs
Retrieve documents
Interact with databases

Agent Security Risks

Risks include:

Unauthorized actions
Excessive permissions
Data leakage
Prompt injection attacks

Securing Agent Workflows

Best practices include:

Least privilege access
Tool restrictions
Approval workflows
Logging and monitoring
Input validation

API Security

AI systems often expose APIs.

API security may include:

Authentication
Authorization
Rate limiting
API gateways
Monitoring

Azure API Management

Azure API Management helps:

Secure APIs
Enforce policies
Monitor usage
Apply throttling

Data Encryption

Encryption protects data:

At rest
In transit

Azure services support encryption by default.

TLS and HTTPS

TLS/HTTPS secure data transmitted across networks.

Secure AI systems should always use encrypted communication.

Compliance and Governance

Organizations may require compliance for:

Healthcare
Finance
Government
Enterprise security policies

Governance Policies

Governance may enforce:

Approved regions
Resource tagging
Security requirements
Allowed configurations

Azure Policy

Azure Policy helps enforce governance standards.

Examples include:

Requiring private endpoints
Blocking public access
Enforcing encryption

Monitoring Security Events

Organizations should monitor:

Failed authentication attempts
Unauthorized access
Suspicious activity
API abuse

Logging and Auditing

Logging supports:

Troubleshooting
Compliance
Security investigations
Audit trails

Security Monitoring Tools

Common tools include:

Azure Monitor
Microsoft Defender for Cloud
Application Insights
Azure Policy

Common AI-103 Security Scenarios

Scenario 1: Enterprise AI Chatbot

Requirements:

Secure document retrieval
Private networking
Keyless authentication

Recommended Security:

Managed identities
Private endpoints
RBAC

Scenario 2: Multi-Agent Enterprise Workflow

Requirements:

Controlled tool execution
Least privilege access
Workflow auditing

Recommended Security:

Custom roles
Logging
Approval controls

Scenario 3: Regulated Industry AI System

Requirements:

Compliance
Encryption
Restricted internet access

Recommended Security:

VNets
Private endpoints
Azure Policy

Scenario 4: Public AI API Platform

Requirements:

API protection
Usage monitoring
Abuse prevention

Recommended Security:

API Management
Rate limiting
Monitoring

Common AI-103 Exam Tips

Understand Managed Identities

Know:

System-assigned identities
User-assigned identities
Keyless authentication

Learn RBAC Concepts

Understand:

Roles
Permissions
Scope
Least privilege

Understand Private Networking

Know:

VNets
Private endpoints
Public vs private access

Learn Secure AI Architecture Principles

Understand:

Secret management
Encryption
Governance
Monitoring

Summary

Security is essential for enterprise AI and agent-based systems.

For the AI-103 exam, you should understand:

Managed identities
Keyless authentication
Azure Key Vault
RBAC and role policies
Private networking
VNets and private endpoints
API security
Secure AI architecture
Governance and compliance
Monitoring and auditing

Strong security practices help ensure AI systems remain:

Secure
Compliant
Reliable
Governed
Protected from misuse

These concepts are foundational for deploying secure AI solutions on Azure.

Azure API Management secures APIs and applies usage policies.

Go to the AI-103 Exam Prep Hub main page