I'm a compliance specialist going all-in on AI governance. Here's why.

AI governance is the work of making sure artificial intelligence is used responsibly, with clear accountability and evidence to back it up. It is not just a technology problem. It is a compliance problem, too.

That is what drew me in.

My background is in federal grants compliance: eligibility checks, reporting requirements, and the steady work of making sure a record tells the same story as the process behind it. ORR reporting taught me that good intentions are not a substitute for good documentation.

Now I see organizations asking new questions. Who approved this AI tool? What data went into it? What happened when its output was wrong? Can we reconstruct the decision six months later?

The vocabulary is different. The discipline is familiar.

Key takeaway An audit trail is an audit trail, whether it's grant dollars or model outputs.

I also work with systems, databases, Knack, and SQL. That matters because a framework only becomes useful when you can translate it into fields, workflows, checks, and records people can actually maintain.

As a doctoral student in Information Technology in Education at the University of Nevada, Reno, I am thinking about where these questions meet learning, institutions, and public trust.

This blog is where I will work through them — in plain English, one practical idea at a time.