What’s the best way to detect shadow AI deployments in Azure: CrowdStrike, Orca, or native controls?

For simply preventing unauthorized Azure resources, a native Azure Policy approach may be more effective than relying on a security product to discover them afterward. You can restrict creation of specific AI resource types and allow exceptions for approved subscriptions, resource groups, or deployment workflows. That handles governance up front, although you’ll still need logging and identity monitoring for misuse of approved resources.

First define what you mean by “shadow AI,” because there are several different problems hiding under that label. One is an unapproved Azure AI resource, another is an approved resource being accessed by an unexpected identity, and a third is an employee sending company data to an external AI service from a workstation. Cloud posture tools are generally strongest at the first two, while endpoint and identity telemetry may be better for the third. I’d run a proof of concept with separate test cases instead of comparing dashboards: create an untagged Azure OpenAI resource, grant excessive permissions, use an unusual identity, and generate an external AI connection from a managed endpoint.

An endpoint-first platform can have blind spots with cloud-native AI services, especially if its inventory depends on endpoint, agent, or limited cloud API data. When evaluating any AI-SPM product, verify that it can enumerate Azure OpenAI resources directly, identify models and deployments, show ownership and permissions, and detect unusual usage or access patterns. A broad AI inventory alone doesn’t necessarily mean it will catch every form of shadow AI.

Orca is worth evaluating if you want deep cloud visibility, but it may be more platform than you need for this specific use case. I’d compare it against native Azure controls and your existing endpoint and identity tools, then choose based on which combination covers both unauthorized cloud resources and data leaving managed devices.