note · Mar 2026
Most enterprise AI risk isn't in a lab — it's in the ordinary workflows people already run. A short look at how to recognize it before you decide what to do about it.
Ask a compliance lead where AI touches their business and you'll often get a short list: the chatbot, maybe a copilot in the productivity suite. Ask again a week later, after they've looked, and the list is longer than anyone expected — a model summarizing contracts here, an enrichment step calling an API there, a spreadsheet macro quietly routing customer data through a service no one reviewed.
None of that was a decision. It accumulated. And that is exactly why it's hard to govern: you can't put rules around what you can't yet see.
The instinct is to reach for a control. But a control you place before you understand the workflow tends to block the wrong thing, or nothing at all. The more useful first move is quieter: watch. See where AI actually sits in the work, what data it touches, and what would happen if it were wrong — before you change anything.
That's the whole idea behind starting an Inspector in Watch mode: it observes a real workflow at zero risk and shows you what it would have caught. No autonomy handed over, nothing rewired. Just a clear picture of where you stand.
This is a scaffold post — proof that the content pipeline builds a real, indexable page from a plain Markdown file, with its own permanent URL, its own metadata, and its own place in the sitemap. The words here are a placeholder for the domain-specific stories that will live in this space next.
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