Historically, shadow IT involved unauthorized SaaS applications, detectable through OAuth grants, network traffic, or expense reports. These tools existed outside the company's core infrastructure, making them external and somewhat containable.
The new form of shadow IT involves engineers using AI agents to stand up infrastructure directly within the company's cloud accounts. This process often occurs without formal tickets, security reviews, or involvement from security teams, as the AI can generate code and provision resources rapidly.
While the intent of engineers is often to help their teams, this rapid deployment can lead to significant security vulnerabilities. An AI-provisioned app might open necessary ports and deploy with over-permissioned IAM roles, creating public-facing endpoints that are easily exploitable. This internal deployment makes detection harder than external SaaS, as it doesn't show up in traditional OAuth logs.
Unlike previous shadow IT, which often involved deliberate circumvention of IT policies, this new trend stems from engineers trying to be efficient with new tools. This makes enforcement difficult, requiring organizations to proactively address the issue rather than reactively contain it. A potential scenario involves a lightweight internal app, built by an AI, leading to a public-facing endpoint with excessive permissions, creating a significant security risk within weeks.
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The rise of AI agents capable of provisioning cloud infrastructure is leading to a new form of "shadow IT," where engineers deploy applications without security oversight. This new challenge differs from traditional SaaS shadow IT because the unauthorized resources reside within the company's cloud account, making detection and containment more difficult and increasing potential security vulnerabilities.