AI governance fails when companies ignore how data moves through workflows
TechNadu · x · 2026-07-24
The article argues that AI governance fails less because of AI itself and more because existing data-security gaps get magnified inside AI workflows.
It highlights five common mistakes organizations make:
- treating governance as policy documents only
- ignoring Shadow AI
- overrelying on legacy DLP controls
- assuming access control alone is enough
- not tracing how sensitive data actually moves through AI systems
The central point is that strong governance has to start from the data flow, not from a simple list of approved or banned tools.
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