🔍Cloud Security Baseline for Azure AI Search

Minimum Security Baseline (MSB) for Azure AI Search

Azure AI Search involves establishing the essential security measures and configurations to safeguard data and services.

These baselines should address identity management, network security, data protection, logging, and compliance.

Use Azure Active Directory (AAD): Integrate Azure AI Search with AAD for centralized identity management.
- Ensure Multi-Factor Authentication (MFA) is enabled for user access.
- Use Conditional Access policies to restrict access based on location, device, or risk levels.
Role-Based Access Control (RBAC):
- Assign least privilege roles to users and applications. Only grant the necessary permissions for accessing Azure AI Search resources.
- Use predefined roles such as Search Contributor or create custom roles if needed.
- Regularly review and audit access rights.

Virtual Network (VNet) Integration:
- Integrate Azure AI Search into a VNet to restrict network access and isolate it from public networks.
- Use Private Endpoints to enable private connectivity to Azure AI Search, avoiding exposure over the internet.
Network Security Groups (NSGs):
- Apply NSGs to restrict inbound and outbound traffic to the resources.
- Define rules to allow only required communication channels and block unnecessary ports.
Azure Firewall:
- Implement Azure Firewall to inspect and control traffic into and out of the VNet hosting Azure AI Search.
- Use Web Application Firewall (WAF) in front of Azure AI Search when exposed through an Application Gateway.

Encryption:
- Data at Rest: Ensure that Azure AI Search uses encryption for data at rest using Azure-managed keys or customer-managed keys (CMKs) through Azure Key Vault.
- Data in Transit: Enforce HTTPS for data transmission to encrypt data between clients and the service.
Data Backup and Recovery:
- Enable backup policies for the underlying data to ensure data recovery in case of accidental deletion or corruption.
- Test recovery processes periodically to ensure they function as expected.

Enable Azure Monitor: Use Azure Monitor to track the health and performance of Azure AI Search.
Diagnostic Logging:
- Enable Diagnostic Logs for Azure AI Search to capture detailed information about queries, indexing, and user activities.
- Store logs in a secure Log Analytics Workspace for analysis.
Alerting:
- Set up Alerts based on specific activities or anomalies in Azure AI Search, such as high query volume or unauthorized access attempts.
- Use Azure Security Center (now known as Microsoft Defender for Cloud) for continuous assessment and alerting on security posture.

Security Center Recommendations: Use Microsoft Defender for Cloud to get recommendations on securing Azure AI Search resources.
Compliance Certifications:
- Ensure that Azure AI Search meets compliance standards such as GDPR, HIPAA, or ISO 27001 based on your organization's requirements.
Resource Tags:
- Use resource tags to categorize and track Azure AI Search resources for easier management and governance.
Azure Policy:
- Apply Azure Policies to enforce specific security configurations across Azure AI Search resources, such as enforcing HTTPS or ensuring resource deployment in specific regions.

Replication: Use replica sets for high availability and disaster recovery of your Azure AI Search instances.
Geo-redundant Storage (GRS): For index data, use Geo-redundant or Zone-redundant storage to ensure data durability and availability in different regions.
Test DR plans: Regularly conduct DR tests to verify that failover procedures for Azure AI Search work as expected.

API Keys: Limit the use of API keys and rotate them regularly. Prefer using AAD OAuth tokens for more secure access.
Rate Limiting: Implement rate limiting for API calls to prevent abuse or unintended overuse of resources.
CORS (Cross-Origin Resource Sharing): Configure CORS policies to allow only trusted domains to interact with your Azure AI Search service.