Which approach is better for detecting shadow AI in Azure: CrowdStrike, Orca, or native controls?

For preventing unauthorized deployments, Azure Policy may be more reliable than trying to detect everything after the fact. You can deny creation of specific AI resource types by default and add exceptions for approved subscriptions, resource groups, or workloads. A cloud-native platform such as Orca may provide broader visibility, but it could be more capability than you need if your main goal is resource governance.

First, define what you mean by shadow AI. There’s a big difference between finding unapproved Azure AI resources, spotting approved resources being accessed by unexpected identities, and detecting employees sending company data to external AI services. Cloud posture tools are strongest at the first two, while endpoint and identity telemetry may be more useful for the third.

I wouldn’t treat one missed Azure OpenAI deployment as proof that an endpoint-first product can’t work. For a proof of concept, create several test cases: an untagged AI resource, excessive permissions, unusual identity access, and an external AI connection from a managed device. Then compare what each product actually detects instead of relying on the inventory dashboard alone. Check whether it supports native Azure OpenAI enumeration and usage anomaly detection.

You could also evaluate Microsoft Defender for Cloud Apps alongside your existing endpoint tooling. It may help cover SaaS and external AI usage, while Azure-native controls handle resource creation and access. In practice, shadow AI usually needs a combination of cloud inventory, identity monitoring, endpoint telemetry, and preventive policy rather than one product.