Tag: Defender for AI Service

AI Governance, AI Security, Microsoft Certification, SC-500, security October 10, 2026

Enable Defender for AI Service in Cloud Workload Protection in Defender for Cloud (SC-500 Exam Prep)

This post is a part of the "SC-500: Implementing End-to-End Security Controls for Cloud and AI Workloads" Exam Prep Hub.
This topic falls under these sections:
Secure compute (20–25%)
   --> Implement security for AI
      --> Enable Defender for AI Service in Cloud Workload Protection in Defender for Cloud

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Overview

Artificial intelligence workloads can introduce security risks that are different from those associated with traditional applications. Examples include unauthorized access to AI services, suspicious model usage, abuse of AI endpoints, anomalous activity, and attacks against applications that consume Azure AI services.

Microsoft Defender for AI Services is a workload protection capability in Microsoft Defender for Cloud designed to detect threats targeting Azure AI services workloads. It complements identity controls, network security, data protection, AI guardrails, and security posture management.

This topic is part of the Secure compute → Implement security for AI area of the SC-500 exam.

What Is Defender for AI Services?

Defender for AI Services provides security monitoring and threat protection for supported Azure AI services workloads. It is part of the broader Cloud Workload Protection Platform capabilities in Microsoft Defender for Cloud.

Its purpose is to help security teams:

Detect suspicious activity involving Azure AI services.
Identify potential threats targeting AI service resources.
Investigate security alerts in the Microsoft Defender portal.
Review AI security posture and coverage.
Combine AI workload protection with broader Defender for Cloud capabilities.
Correlate AI-related security information with other incidents and alerts.

Defender for AI Services is not a replacement for Microsoft Foundry guardrails, Azure AI Content Safety, Microsoft Entra ID, Azure Policy, or network controls. Instead, it adds a security monitoring and threat-detection layer to the AI workload.

AI Workload Security: Posture Versus Runtime Protection

A key exam concept is the difference between security posture management and runtime threat protection.

Cloud Security Posture Management

Cloud Security Posture Management, or CSPM, focuses on identifying and reducing configuration risks before they result in an incident.

Examples include:

An AI service that permits unnecessary public network access.
Local authentication being enabled when Microsoft Entra authentication should be used.
Excessive permissions assigned to an application or identity.
Missing security configuration or governance controls.
Resources that do not comply with organizational policies.

Cloud Workload Protection

Cloud Workload Protection, or CWP, focuses on detecting threats and suspicious behavior while workloads are operating.

Examples include:

Suspicious activity targeting an AI service.
Abnormal usage patterns.
Potential attempts to exploit an AI workload.
Threat indicators associated with an AI service resource.
Runtime activity that requires investigation.

Microsoft Defender for Cloud combines discovery, posture management, and runtime protection to provide broader visibility into AI environments.

Exam distinction

If a question asks which capability identifies a misconfiguration, think primarily of CSPM.

If it asks which capability detects a threat or suspicious activity during operation, think primarily of CWP, including Defender for AI Services.

Supported AI Workload Context

The learning material specifically associates this capability with Azure AI services workloads, including services such as:

Azure OpenAI-related workloads.
Microsoft Foundry and AI service resources.
AI model deployments and related service endpoints.
Applications that consume Azure AI services.

The exact supported resource types and detections can change as Microsoft expands the service. Therefore, organizations should verify current service coverage and supported regions before designing a production deployment.

Defender for AI Services should be considered part of a layered security architecture rather than a single control that secures every component of an AI solution.

Prerequisites

Before enabling Defender for AI Services, administrators should have:

An Azure subscription containing the AI workloads to protect.
Microsoft Defender for Cloud enabled for the subscription.
Appropriate permissions to configure Defender for Cloud plans.
Familiarity with Azure AI services and model deployments.
Familiarity with the Azure portal and Microsoft Defender portal.

The associated Microsoft Learn module identifies an Owner or Contributor role on the target subscription as a prerequisite for the configuration exercise. In production environments, organizations should use the least-privileged role that provides the required administrative capability.

Enable Defender for AI Services

The plan is enabled from the Defender for Cloud environment settings.

Step 1: Open Microsoft Defender for Cloud

Sign in to the Azure portal.
Search for and open Microsoft Defender for Cloud.
Select Environment settings.

Step 2: Select the subscription

Select the Azure subscription that contains the AI workloads.
Review the available Defender for Cloud plans.

Defender for Cloud plans can be enabled at the subscription level. Enabling a plan at subscription scope generally applies the protection to applicable resources within that subscription.

Step 3: Enable the AI Services plan

Locate the plan for Defender for AI Services.
Turn the plan on.
Review any available plan-specific configuration options.
Select Save.

The exact portal labels and available configuration options may change as the service evolves. The important exam concept is that Defender for AI Services is enabled as a Defender for Cloud workload protection plan, rather than by installing a traditional agent on each AI service resource.

Configure Plan Components

After enabling the plan, review its available components and configuration settings.

Depending on the current service capabilities, configuration may include:

Selecting which AI workloads are covered.
Reviewing supported AI service resource types.
Enabling or disabling available protection components.
Configuring notification and monitoring integrations.
Reviewing the subscription’s protection status.
Confirming that the required security data is available.

Microsoft continuously adds capabilities to Defender for Cloud plans. Azure Policy includes a built-in initiative named Configure Microsoft Defender threat protection for AI Services to be enabled, which can help ensure that newly created or existing subscriptions remain configured according to organizational requirements.

Important distinction

The Defender for AI Services plan provides the protection capability. Azure Policy can help enforce or audit the desired configuration.

These are different functions:

Capability	Primary purpose
Defender for AI Services	Detect threats targeting AI services workloads
Azure Policy	Audit or enforce resource configuration
Microsoft Defender for Cloud CSPM	Identify security posture weaknesses
Microsoft Foundry guardrails	Apply controls to AI inputs, outputs, and model behavior
Microsoft Entra ID	Authenticate and authorize users, applications, and identities
Private Link and network controls	Reduce network exposure

Monitor AI Security with the Data and AI Security Dashboard

After the plan is enabled, use the Data and AI security dashboard in Microsoft Defender for Cloud to review AI security information.

The dashboard is intended to provide visibility into areas such as:

AI resources discovered in the environment.
Security posture information.
Protection coverage.
Security recommendations.
AI-related alerts and findings.
Potential risks affecting AI workloads.

The dashboard helps security teams understand whether AI resources are being protected and where additional action may be required.

Recommended monitoring process

Review the AI resource inventory.
Confirm that expected subscriptions and resources are represented.
Review recommendations and unresolved security issues.
Investigate active alerts.
Determine whether the issue is a configuration problem, an identity problem, a network problem, or a runtime threat.
Remediate the issue.
Confirm that the resource returns to the expected protection state.

Investigate AI Threat Protection Alerts

Defender for AI Services can generate security alerts when suspicious activity associated with supported AI workloads is detected.

When investigating an alert, review:

The affected subscription.
The affected AI service or resource.
The alert severity.
The detection time.
The activity associated with the alert.
The identity or application involved, when available.
Related resources and incidents.
Recommended remediation actions.

AI-related alerts can be investigated through the Microsoft Defender portal. Defender for Cloud alerts can also integrate with Microsoft Defender XDR, allowing security operations teams to correlate cloud alerts with identity, endpoint, email, and other security signals.

Example investigation workflow

A security analyst notices suspicious activity associated with an AI service.

Open the alert in the Defender portal.
Review the affected AI resource.
Examine the evidence and activity timeline.
Identify the application, identity, or network source involved.
Determine whether the activity is expected.
Disable or restrict a compromised identity if necessary.
Rotate exposed credentials.
Review network access and authentication configuration.
Investigate related resources and incidents.
Document the remediation and verify that the threat is no longer present.

Relationship to Other AI Security Controls

Defender for AI Services should be deployed as part of defense in depth.

Microsoft Entra ID

Use Microsoft Entra ID to control who or what can access AI services.

Recommended controls include:

Microsoft Entra authentication.
Managed identities.
Role-based access control.
Conditional Access where applicable.
Least-privilege permissions.
Removal of unnecessary credentials.

Defender for AI Services may detect suspicious activity, but it does not replace proper identity configuration.

Azure AI Content Safety and Foundry Guardrails

Guardrails help control unsafe or undesirable AI inputs and outputs. They address risks such as:

Harmful content.
Prompt-based abuse.
Inappropriate model responses.
Content filtering requirements.
Certain application-level AI risks.

Runtime threat protection and AI guardrails address different security concerns. A workload can have guardrails configured and still require threat monitoring.

Azure Policy

Azure Policy can audit or enforce requirements such as:

AI services should have local authentication disabled.
AI services should restrict network access.
Defender for AI Services should be enabled.

For example, Microsoft provides policy definitions related to disabling key-based access and restricting network access for Azure AI Services resources.

Network security

Network controls can reduce exposure by using:

Private endpoints.
Virtual network integration where supported.
Network access restrictions.
Firewall rules.
Private DNS configuration.
Restricted administrative access.

Network restrictions reduce the attack surface, while Defender for AI Services helps detect threats against the workload.

Microsoft Defender XDR

Defender XDR can provide a broader incident investigation experience by correlating AI workload alerts with other security signals.

Subscription-Level Enablement and Scale

Defender for Cloud plans are commonly configured at subscription scope. Organizations with many subscriptions should consider centralized governance.

Possible approaches include:

Enabling the plan on individual subscriptions.
Using management groups to organize subscriptions.
Applying Azure Policy initiatives.
Auditing plan coverage.
Reviewing coverage workbooks.
Establishing a standard for newly created subscriptions.

The Defender for Cloud coverage workbook helps administrators understand which plans are enabled across subscriptions and resources.

Why centralized governance matters

Without centralized governance, an organization may have:

AI resources deployed in subscriptions without protection.
Inconsistent security configurations.
Newly created resources that are not covered.
Different teams using different security standards.
Gaps between development, test, and production environments.

Azure Policy can help maintain consistency, but policy compliance should be verified rather than assumed.

Common Troubleshooting Issues

The plan is not visible

Possible causes include:

The wrong subscription or environment was selected.
The user lacks sufficient permissions.
The capability is not available in the selected region.
The service or plan name has changed.
The feature is subject to preview or availability limitations.

AI resources are not appearing in the dashboard

Check:

Whether the correct subscription is selected.
Whether the plan is enabled.
Whether the resource type is supported.
Whether the resource is in a supported region.
Whether sufficient time has passed for discovery and data collection.
Whether the resource is excluded by configuration or policy.

Alerts are not appearing

Check:

Whether the plan is enabled for the correct subscription.
Whether the activity matches a supported detection.
Whether the resource is covered.
Whether the alert is being viewed in the correct portal.
Whether filters are hiding the alert.
Whether the issue is a posture recommendation rather than a runtime alert.

A resource is secure but still has recommendations

This may occur because:

The recommendation has not refreshed.
The resource has another unresolved configuration issue.
A policy assignment requires a different setting.
The resource is evaluated against a broader security standard.
The recommendation applies to a different component of the workload.

Best Practices

Enable protection before production deployment

Do not wait until an AI service is compromised before enabling monitoring and threat protection.

Use least privilege

Assign only the permissions required to configure Defender for Cloud and manage AI resources.

Combine CSPM and CWP

Use CSPM to reduce misconfigurations and CWP to detect suspicious runtime activity.

Restrict network exposure

Use private endpoints and network restrictions where supported and appropriate.

Prefer Microsoft Entra authentication

Avoid unnecessary use of static keys. Use managed identities or Microsoft Entra authentication when supported.

Enforce configuration with Azure Policy

Use policy to audit or enforce requirements such as:

Defender for AI Services being enabled.
Local authentication being disabled.
Network access being restricted.

Monitor the Data and AI dashboard

Review coverage, recommendations, and alerts regularly.

Integrate with incident response

Ensure that AI security alerts are routed to the appropriate security operations team and correlated with other incidents.

Do not assume that one control solves every AI risk

AI security requires multiple layers, including:

Identity.
Network security.
Data protection.
Application security.
Guardrails.
Runtime threat detection.
Logging and monitoring.
Governance and compliance.

Exam-Focused Comparisons

Exam concept	Correct interpretation
Defender for AI Services	Runtime threat protection for supported Azure AI services workloads
AI workloads plan	Defender for Cloud plan used to protect AI workloads
Data and AI security dashboard	View AI security posture, resources, and protection information
CSPM	Identifies configuration and posture weaknesses
CWP	Detects threats and suspicious runtime activity
Azure Policy	Audits or enforces Azure resource configuration
Foundry guardrails	Controls AI behavior, inputs, and outputs
Microsoft Entra ID	Provides authentication and authorization
Defender XDR	Correlates and investigates security signals across workloads
Coverage workbook	Helps verify Defender for Cloud plan coverage

Practice Exam Questions

Question 1

An organization uses Azure AI services for a customer-support application. The security team wants to detect suspicious activity targeting the AI service while the application is running. Which capability should the team enable?

A. Azure Policy
B. Microsoft Defender for AI Services
C. Microsoft Entra Privileged Identity Management
D. Azure Resource Manager locks

Answer: B

Explanation: Microsoft Defender for AI Services is designed to detect threats targeting supported Azure AI services workloads. Azure Policy governs configuration, PIM manages privileged access, and resource locks help prevent accidental deletion or modification.

Question 2

Where should an administrator go to enable Defender for AI Services for an Azure subscription?

A. Microsoft Foundry project settings
B. Azure Monitor Workbooks
C. Microsoft Defender portal Incidents page
D. Microsoft Defender for Cloud Environment settings

Answer: D

Explanation: Defender for Cloud workload protection plans are enabled through Microsoft Defender for Cloud → Environment settings, where the administrator selects the appropriate subscription and enables the required plan.

Question 3

A security engineer wants to identify an AI service that has an insecure configuration, such as unnecessary public network access. Which Defender for Cloud capability is most directly relevant?

A. Cloud Security Posture Management
B. Cloud Workload Protection
C. Microsoft Defender XDR incident correlation
D. Azure Bastion

Answer: A

Explanation: CSPM identifies configuration weaknesses and security posture risks. CWP focuses on runtime threats, Defender XDR supports investigation and correlation, and Azure Bastion provides secure administrative access to virtual machines.

Question 4

After enabling Defender for AI Services, which feature should an administrator use to review AI resource insights, security posture, and protection information?

A. Azure Service Health
B. Azure Advisor only
C. Data and AI security dashboard
D. Azure Cost Management

Answer: C

Explanation: The Data and AI security dashboard in Defender for Cloud provides visibility into AI resources, security posture, and related protection information.

Question 5

An organization wants to ensure that Defender for AI Services remains enabled across newly created subscriptions. Which approach is most appropriate?

A. Configure a resource lock on every AI service
B. Create a custom Microsoft Entra authentication method
C. Enable Azure Bastion
D. Use Azure Policy to audit or deploy the required Defender for AI Services configuration

Answer: D

Explanation: Azure Policy can help audit or enforce the desired Defender for Cloud plan configuration across a defined scope. Resource locks, authentication methods, and Bastion do not ensure that the Defender for AI Services plan is enabled.

Question 6

Which statement best describes the relationship between Defender for AI Services and Microsoft Foundry guardrails?

A. Defender for AI Services replaces all Foundry guardrails
B. Defender for AI Services detects workload threats, while guardrails help control AI inputs, outputs, and behavior
C. Foundry guardrails are used only to enable Azure subscriptions
D. Defender for AI Services is required only for virtual machines

Answer: B

Explanation: These controls address different risks. Defender for AI Services provides workload threat protection, while Foundry guardrails help manage AI behavior and content-related risks.

Question 7

A security analyst receives an alert involving suspicious activity against an Azure AI service. Where should the analyst investigate the alert?

A. Microsoft Defender portal
B. Azure Storage Explorer
C. Azure Resource Graph only
D. Microsoft Entra Domain Services

Answer: A

Explanation: Defender for AI Services alerts can be investigated in the Microsoft Defender portal. Defender for Cloud alerts can also integrate with Microsoft Defender XDR for broader correlation and investigation.

Question 8

Which statement about Defender for AI Services is correct?

A. It eliminates the need for identity and network controls
B. It encrypts every prompt and model response automatically
C. It provides runtime threat protection for supported Azure AI services workloads
D. It is an Azure resource lock mechanism

Answer: C

Explanation: Defender for AI Services is a workload protection capability. It does not replace authentication, authorization, encryption, network restrictions, or other defense-in-depth controls.

Question 9

An administrator enables Defender for AI Services but does not see a particular AI resource in the dashboard. What should the administrator check first?

A. Whether the resource is supported, in the correct subscription, and in a supported region
B. Whether the resource has an Azure Bastion host
C. Whether the resource has a resource lock
D. Whether the application uses a virtual machine scale set

Answer: A

Explanation: Missing resource visibility can result from unsupported resource types, incorrect subscription selection, regional availability, or discovery delays. Bastion, resource locks, and VM scale sets are not prerequisites for discovering an AI service resource.

Question 10

Which combination provides the most complete defense-in-depth approach for an Azure AI workload?

A. Resource locks and Azure Cost Management
B. Azure Bastion and storage replication
C. Azure Policy only
D. Microsoft Entra authentication, network restrictions, AI guardrails, Defender for Cloud posture management, and Defender for AI Services runtime protection

Answer: D

Explanation: AI workloads require multiple complementary controls. Identity protects access, network restrictions reduce exposure, guardrails address AI behavior, CSPM identifies configuration weaknesses, and Defender for AI Services detects runtime threats.

Key Takeaways

For the SC-500 exam, remember the following:

Defender for AI Services is a Defender for Cloud workload protection capability.
It focuses on detecting threats targeting supported Azure AI services workloads.
Enable it through Microsoft Defender for Cloud → Environment settings.
Use the Data and AI security dashboard to monitor AI security information.
Investigate alerts in the Microsoft Defender portal.
Use CSPM for configuration and posture risks.
Use CWP for runtime threat detection.
Use Azure Policy to audit or enforce plan configuration.
Defender for AI Services complements—not replaces—identity, network, guardrail, and data security controls.
Treat AI security as a defense-in-depth responsibility rather than a single-product task.

Go to the SC-500 Exam Prep Hub main page

AI Security, azure, Azure AI, Microsoft Certification, SC-500, security October 10, 2026

Monitor AI security by using the Data and AI security dashboard in Defender for Cloud (SC-500 Exam Prep)

This post is a part of the "SC-500: Implementing End-to-End Security Controls for Cloud and AI Workloads" Exam Prep Hub.
This topic falls under these sections:
Secure compute (20–25%)
   --> Implement security for AI
      --> Monitor AI security by using the Data and AI security dashboard in Defender for Cloud

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Overview

Artificial intelligence workloads introduce security risks that are different from those associated with traditional applications. AI systems may process sensitive data, use external tools, connect to storage and search services, expose model endpoints, and depend on containers, libraries, identities, and infrastructure-as-code configurations.

Microsoft Defender for Cloud provides capabilities for discovering AI workloads, assessing their security posture, detecting threats, and investigating security issues. The Data and AI security dashboard provides a centralized view of data and AI resources, their protection coverage, security recommendations, alerts, attack paths, and internet exposure.

For the SC-500 exam, the key concept is that the dashboard helps security teams answer three questions:

What data and AI resources exist in the environment?
What security risks or protection gaps affect those resources?
What actions should be taken to reduce the risks?

The dashboard combines information from Defender for Cloud capabilities such as Cloud Security Posture Management, sensitive data discovery, Defender for Storage, Defender for Databases, and AI threat protection.

What Is the Data and AI Security Dashboard?

The Data and AI security dashboard is a centralized monitoring experience in Microsoft Defender for Cloud. It provides visibility into an organization’s data and AI estate and helps security teams identify resources that require attention.

The dashboard can display information about:

Storage resources
Managed databases
Hosted databases, including databases hosted on infrastructure
AI services and AI workloads
Sensitive data
Security recommendations
Security alerts
Attack paths
Internet-exposed resources
Protection coverage
AI threat detection activity

The dashboard is intended to support both proactive and reactive security activities:

Proactive security: Identify misconfigurations, exposed resources, missing protection, and potential attack paths.
Reactive security: Investigate alerts, identify affected resources, and respond to detected threats.

It is important to understand that the dashboard is not itself a replacement for all security controls. Instead, it provides a consolidated view of information collected by Defender for Cloud and related security services.

Relationship to AI Security Posture Management

Microsoft Defender for Cloud includes AI security posture management, which helps organizations discover AI workloads and identify security risks throughout the AI lifecycle.

AI security posture management can help identify:

AI applications and services
AI models and model deployments
Vulnerable AI-related libraries
Infrastructure-as-code misconfigurations
Internet-exposed AI endpoints
Weak or excessive identity permissions
Risks involving data used for grounding or fine-tuning
Potential attack paths involving AI resources

Defender for Cloud can discover AI workloads across supported environments and services, including Azure AI services, Azure AI Foundry, Azure Machine Learning, Amazon Bedrock, and Google Vertex AI.

The dashboard provides a practical way to review these findings without having to examine every AI resource independently.

AI security posture management versus AI threat protection

These capabilities address different security questions.

Capability	Primary purpose
AI security posture management	Identify configuration weaknesses, vulnerabilities, exposure, and security gaps
AI threat protection	Detect suspicious or malicious activity targeting AI workloads
Data and AI security dashboard	Present data and AI inventory, posture findings, protection coverage, and threat information in one view

For example:

A publicly accessible AI endpoint is primarily a posture concern.
A suspicious sequence of prompts targeting an AI service may be a runtime threat concern.
The dashboard can help security personnel see both types of information together.

Prerequisites for the Dashboard

The exact information displayed depends on the Defender for Cloud plans and capabilities enabled in the subscription.

For full access to the dashboard’s data and AI capabilities, Microsoft documentation identifies the following requirements:

Defender CSPM
The Defender CSPM sensitive data discovery extension
Defender for Storage
Defender for Databases
AI workload threat protection
Registration of each relevant Azure subscription with the Microsoft.Security resource provider

Access also requires appropriate permissions to read assessments, subassessments, and alerts. The documented minimum privileged role for the dashboard is the Security reader role.

Why plan enablement matters

If a protection plan is not enabled, the dashboard may show incomplete protection coverage or may not provide certain findings.

For example:

Without sensitive data discovery, sensitive information findings may be unavailable.
Without Defender for Storage, storage threat protection information may be incomplete.
Without Defender for Databases, database-related protection information may be unavailable.
Without AI threat protection, AI prompt scanning and AI threat alerts may not be displayed.

The dashboard should therefore be interpreted in the context of the plans enabled for the subscription.

Data and AI Security Overview

The Data and AI security overview section provides a high-level view of the organization’s data and AI resources.

Resources may be categorized into areas such as:

Storage assets
Managed databases
Hosted databases
AI services

The overview can help identify whether resources are:

Fully protected
Partially protected
Not protected

Protection status depends on the relevant Defender for Cloud plans and extensions that apply to the resource.

The overview may also highlight resources associated with:

High-severity recommendations
Critical-severity recommendations
High-severity alerts
Critical-severity alerts
High- or critical-severity attack paths

This allows security teams to prioritize the most important issues instead of treating every resource equally.

The Top Issues Section

The Top issues section focuses attention on the resources and findings that require the most immediate action.

It can include:

High- and critical-severity alerts

Alerts indicate that Defender for Cloud or an integrated protection service has detected potentially malicious or suspicious activity.

Examples may include:

Suspicious activity involving data resources
Threats against AI workloads
Malicious or abnormal access patterns
Other detected security events

Alerts should be investigated to determine:

Which resource is affected
What activity was detected
When the activity occurred
Whether the activity is ongoing
What identities or services were involved
Whether containment or remediation is required

High- and critical-severity recommendations

Recommendations identify security improvements that should be made to reduce risk.

Examples include recommendations related to:

Internet exposure
Authentication
Identity permissions
Data protection
Missing security configurations
Vulnerable components
Protection plan coverage

A recommendation is generally a posture finding, whereas an alert is generally associated with detected activity or a security event.

High- and critical-severity attack paths

Attack path analysis helps identify combinations of weaknesses that could allow an attacker to reach a valuable resource.

An attack path may involve:

An internet-exposed endpoint
A weak identity or excessive permission
A vulnerable workload
Access to sensitive data
A high-value AI or data resource

Attack paths are important because an individual issue may appear moderate when viewed in isolation but become critical when combined with other weaknesses.

The Data Closer Look Section

The Data closer look section provides more detailed information about data resources and their associated risks.

It can include the following areas.

Sensitive data discovery

Sensitive data discovery helps identify data resources that contain sensitive information.

Examples of sensitive information may include:

Personal information
Financial information
Credentials or secrets
Regulated information
Other information types identified by the organization

The dashboard can provide an overview of:

Sensitive information types
Sensitivity labels
Resources containing sensitive data
Resources where sensitive data may be exposed

Security teams can use this information to determine whether sensitive data is:

Publicly exposed
Accessible by inappropriate identities
Used by an AI workload without sufficient controls
Stored in a resource lacking appropriate protection

Sensitivity settings can be managed to control which information types and sensitivity labels are relevant to the organization.

Data threat protection

This area provides information about security alerts associated with data resources, including:

Storage resources
Managed databases

The purpose is to help security teams investigate threats affecting data and determine whether the associated resource requires remediation.

Data queries in Security Explorer

The dashboard can provide investigation queries for data-related risks.

Examples of investigation scenarios include:

Data resources containing plaintext secrets
Databases accessible by external users
Public storage containing sensitive data
Resources with potentially unsafe configurations

Security Explorer can be used to investigate relationships between resources, identities, configurations, and risks.

Internet-exposed data resources

The dashboard can identify data resources exposed to the internet.

These may include:

Public storage resources
Internet-accessible managed databases
Hosted databases with external exposure

Internet exposure does not automatically mean that a resource has been compromised. However, it increases the potential attack surface and should be evaluated alongside authentication, authorization, network controls, and data sensitivity.

The AI Closer Look Section

The AI closer look section focuses specifically on AI workloads and their security risks.

It includes several important areas.

AI discovery

AI discovery provides an inventory of AI resources found in the environment.

This visibility helps organizations identify:

Which AI services are deployed
Where AI workloads are running
Which teams or applications use AI
Which AI resources require security assessment
Whether unauthorized or unexpected AI resources exist

AI discovery is particularly important because organizations may have AI resources deployed by multiple teams across different subscriptions, projects, or cloud providers.

AI threat protection

AI threat protection provides information about detected threats involving AI workloads.

The dashboard can display:

The number of prompts scanned
Detected alerts
Alert severity
AI workloads associated with the alerts

Prompt scanning and AI threat detection help identify suspicious activity targeting AI services. However, the number of prompts scanned is a monitoring metric, not a security score by itself.

A high number of scanned prompts may simply indicate that an AI service is heavily used. Security teams should focus on the associated alerts, severity, affected resources, and investigation details.

AI queries in Security Explorer

The dashboard can provide queries for investigating AI-related risks.

Examples include:

Sensitive data resources used for grounding
Vulnerable containers used by AI workloads
AI resources with risky configurations
Relationships between AI endpoints and data resources
AI workloads with excessive exposure or permissions

These queries help security teams understand how AI resources interact with the rest of the environment.

Internet-exposed resources used for grounding

Grounding allows an AI workload to use external data to improve the relevance or accuracy of its responses.

The dashboard can identify internet-exposed storage and search resources used for grounding.

This is important because an AI application may be secure at the model endpoint while the data used to ground the model is exposed or inadequately protected.

Security teams should evaluate:

Whether the grounding data is sensitive
Whether the storage or search resource is publicly accessible
Whether the AI workload has excessive access
Whether authentication and authorization are properly configured
Whether the data source is trustworthy
Whether the resource is protected by appropriate Defender plans

Monitoring AI Protection Coverage

Protection coverage indicates whether resources are protected by the applicable security plans.

A resource may be:

Fully protected

The relevant posture and threat protection capabilities are enabled and providing coverage.

Partially protected

Some relevant capabilities are enabled, but one or more protections are missing.

Not protected

The applicable protection capabilities are not enabled or do not cover the resource.

Protection coverage should be reviewed regularly because AI environments change quickly. New AI services, model deployments, storage resources, and containers may be introduced without being included in the organization’s original security design.

How to Access and Use the Dashboard

A typical workflow is:

Sign in to the Azure portal.
Open Microsoft Defender for Cloud.
Select Data and AI security dashboard.
Review the Data and AI security overview.
Review the Top issues section.
Examine AI discovery and AI threat protection information.
Investigate relevant recommendations, alerts, or attack paths.
Use Security Explorer queries for deeper analysis.
Remediate configuration issues.
Reassess the dashboard to confirm that risk and protection coverage have improved.

For data-specific investigations, the dashboard can also be used to view resources containing sensitive information and then open the resource’s recommendations and alerts.

Recommended Monitoring Process

A repeatable monitoring process can be organized into five stages.

1. Establish an inventory

Identify all AI services, applications, models, endpoints, data sources, containers, and supporting infrastructure.

2. Review protection coverage

Determine whether each resource is covered by the appropriate Defender for Cloud plans.

3. Prioritize critical findings

Start with:

Critical alerts
Critical recommendations
Critical attack paths
Internet-exposed AI endpoints
Sensitive data used by AI workloads
AI resources with excessive permissions

4. Investigate relationships

Use Security Explorer and attack path analysis to understand how an issue could affect other resources.

5. Remediate and verify

Apply the recommended changes, then return to the dashboard to confirm that the issue has been resolved or that the risk has been reduced.

Common Security Actions After Reviewing the Dashboard

Depending on the findings, remediation may include:

Enabling the appropriate Defender for Cloud plan
Removing unnecessary public network access
Configuring private endpoints
Strengthening authentication
Applying least-privilege permissions
Using managed identities
Protecting storage and databases
Removing plaintext secrets
Updating vulnerable libraries or container images
Restricting access to grounding data
Investigating suspicious AI prompts or alerts
Reviewing attack paths
Applying security recommendations through infrastructure as code
Monitoring the environment continuously

The dashboard helps identify what should be addressed, but remediation usually occurs in the underlying Azure service, identity platform, network configuration, data platform, or application.

Important Exam Distinctions

Dashboard versus Security Explorer

The Data and AI security dashboard provides a summarized monitoring view.
Security Explorer provides deeper investigation and relationship analysis.

Recommendation versus alert

A recommendation identifies a security improvement or configuration gap.
An alert indicates detected suspicious or malicious activity.

Posture management versus threat protection

Posture management focuses on reducing weaknesses before they are exploited.
Threat protection focuses on detecting threats and suspicious activity.

AI discovery versus AI threat protection

AI discovery identifies AI resources and workloads.
AI threat protection detects threats targeting those workloads.

Sensitive data discovery versus data threat protection

Sensitive data discovery identifies resources containing sensitive information.
Data threat protection identifies security threats involving data resources.

Internet exposure versus compromise

An internet-exposed resource is not necessarily compromised. It is a resource with increased attack surface and potentially greater risk. An alert or investigation is needed to determine whether malicious activity occurred.

Key Takeaways

For the SC-500 exam, remember these points:

The Data and AI security dashboard provides a centralized view of data and AI security.
The dashboard combines inventory, protection coverage, recommendations, alerts, and attack paths.
Full functionality depends on the relevant Defender for Cloud plans and extensions.
AI discovery helps identify AI resources across the environment.
AI threat protection provides visibility into scanned prompts and detected alerts.
Sensitive data discovery helps identify resources containing sensitive information.
Security Explorer supports deeper investigation of data and AI risks.
Attack path analysis helps identify chains of weaknesses that could lead to high-impact compromise.
Internet-exposed AI and data resources should be prioritized for review.
The dashboard supports monitoring and prioritization; remediation is performed in the underlying services and security controls.

Practice Exam Questions

Question 1

A security team wants a centralized view of its AI resources, protection coverage, recommendations, alerts, and attack paths. Which Microsoft Defender for Cloud capability should the team use?

A. Microsoft Defender Vulnerability Management
B. Azure Resource Graph
C. Data and AI security dashboard
D. Microsoft Sentinel workbook

Correct answer: C

Explanation: The Data and AI security dashboard provides a centralized view of data and AI resources, protection status, recommendations, alerts, and attack paths. Azure Resource Graph can query resources, but it does not provide the same integrated security dashboard experience.

Question 2

An organization wants to identify storage and database resources that contain sensitive information. Which capability should be enabled?

A. Azure Bastion
B. Defender for Servers
C. Sensitive data discovery
D. Microsoft Entra Privileged Identity Management

Correct answer: C

Explanation: Sensitive data discovery identifies resources containing sensitive information types and sensitivity labels. The results can be reviewed through the Data and AI security dashboard.

Question 3

What is the primary purpose of AI security posture management in Defender for Cloud?

A. To identify AI workload risks, vulnerabilities, misconfigurations, and exposure
B. To replace Microsoft Entra authentication for AI applications
C. To train foundation models
D. To provide end-user prompt authoring assistance

Correct answer: A

Explanation: AI security posture management focuses on discovering AI workloads and identifying security weaknesses such as vulnerable components, excessive permissions, misconfigurations, and internet exposure.

Question 4

The AI closer look section reports the number of prompts scanned and displays alerts by severity. What capability is providing this information?

A. Sensitive data discovery
B. AI threat protection
C. Azure Policy
D. Defender for Containers

Correct answer: B

Explanation: AI threat protection provides information about AI-related threat detection, including prompt scanning activity and detected alerts.

Question 5

A security analyst sees a critical recommendation for an AI endpoint that is accessible from the internet. What does this finding primarily represent?

A. Proof that the endpoint has been compromised
B. A completed incident investigation
C. A posture or configuration risk requiring remediation
D. A successful model deployment

Correct answer: C

Explanation: An internet-exposed endpoint is a security posture concern. It increases the attack surface but does not, by itself, prove that a compromise has occurred.

Question 6

Which dashboard section is most directly associated with identifying sensitive information types and sensitivity labels in cloud data resources?

A. Data closer look
B. Top issues
C. AI discovery
D. AI threat protection

Correct answer: A

Explanation: The Data closer look section includes sensitive data discovery information, including sensitive information types and sensitivity labels.

Question 7

A security team wants to investigate whether sensitive data resources are being used for AI grounding. Which capability should the team use?

A. Azure Backup
B. Security Explorer queries
C. Azure Bastion
D. Microsoft Entra authentication methods

Correct answer: B

Explanation: Security Explorer provides queries for investigating relationships and risks involving AI resources, including sensitive data resources used for grounding.

Question 8

Which statement best describes an attack path in Defender for Cloud?

A. A list of all prompts sent to an AI model
B. A backup copy of an affected resource
C. A sequence of weaknesses that could allow an attacker to reach a valuable resource
D. A list of approved Azure Policy definitions

Correct answer: C

Explanation: Attack path analysis identifies connected weaknesses, such as internet exposure, excessive permissions, and vulnerable resources, that could lead to a high-impact compromise.

Question 9

A subscription has Defender CSPM enabled, but sensitive data discovery and Defender for Storage are not enabled. What is the most likely result?

A. The dashboard will automatically protect all storage resources
B. The dashboard may provide incomplete data protection and sensitive data information
C. All storage resources will be removed from the inventory
D. AI threat protection will be disabled automatically

Correct answer: B

Explanation: Dashboard information depends on the relevant plans and extensions. Without sensitive data discovery and Defender for Storage, data-related visibility and protection coverage may be incomplete.

Question 10

Which action is the best first step after identifying a critical AI security alert in the Data and AI security dashboard?

A. Delete every AI resource in the subscription
B. Ignore the alert until the next monthly review
C. Disable all network connectivity
D. Investigate the alert, affected resource, activity, and related recommendations

Correct answer: D

Explanation: A critical alert should be investigated to understand the detected activity, affected resources, identities, timing, and recommended response. Remediation should be based on the investigation rather than automatically deleting or disabling unrelated resources.

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