You are building a web app named App1 that generates responses by using a model...

The correct solution is to configure an Azure AI Search connection in Project1 and reference that connection from each application. Microsoft Foundry project connections are intended to centralize external resource configuration for a project. The official Foundry guidance states that when adding a connection, you select the external service, such as Azure AI Search , and choose the authentication method for that resource. This directly satisfies the requirement for multiple client applications to use the same search configuration instead of duplicating endpoints, indexes, and credentials in each app.

The security requirement is met by configuring the connection with keyless authentication. The Azure AI Search integration guidance specifies that a Foundry project connection requires the search endpoint and either key-based authentication or keyless authentication with Microsoft Entra ID . For keyless authentication, RBAC roles are assigned to the project’s managed identity, eliminating hard-coded API keys.

Options A and C use secure identity-based access but force every client application to maintain its own Azure AI Search configuration, increasing administration and violating the shared-configuration requirement. Option B also increases administrative work and does not use the native Azure AI Search connection type. Reference topics: Microsoft Foundry project connections, Azure AI Search grounding, Microsoft Entra ID authentication, managed identities, and RBAC.