Your central team deployed two models and three documented workflows. Clean architecture diagram. Fits on one slide.

In reality every department has quietly wired those same approved models into their own stack. Marketing auto-generates copy without review. Operations reclassifies tickets using prompts nobody outside their team has seen. Finance summarizes contracts from templates stored in personal Google Drives.

Nobody did anything malicious. They used approved tools in ways your governance layer cannot see.

This episode walks through the difference between shadow IT and shadow workflows and why the second one is harder to detect and more dangerous.

Shadow IT was unapproved tools. Your security team learned to catch those. Shadow workflows are approved tools used in unapproved ways. The API key is valid. The permissions are correct. Your governance layer sees an approved tool being used by authorized users. What it does not see is what those users are asking the tool to do, what prompts they are using, what data they are feeding in, and what decisions are being made on the outputs.

We cover how shadow workflows create conflicting sources of truth across departments. Why they are invisible to traditional governance frameworks. How they surface during audits, incidents, and data events. And what observability across your full AI estate actually requires.

If your visibility into AI usage stops at what the central team deployed, you are missing half the picture.

Keywords: shadow AI, shadow workflows, AI governance, AI compliance, AI observability, enterprise AI, LLMOps, MLOps, AI security, AI audit, unauthorized AI workflows, AI risk management, AI orchestration, AI infrastructure, CTO, CISO, regulated industries, healthcare AI, financial services AI, legal AI

This is Maya. New episodes three times a week.

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