Foundations and Data Discipline
A working command of machine learning, written for the people asked to govern it. Real case studies, plain language, no math anxiety.
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340 pages
For years the AI conversation has belonged to the people who build the models. That era is ending. Regulators are moving, boards are asking hard questions, and the accountability is landing on your desk — not the data scientists’.
This book gives you a working command of the systems you’re being asked to govern: what they are, how they fail, and where — in the pipeline — governance actually lives. Without asking you to become an engineer.
Who now audit AI systems without a technical foundation.
Facing new AI-specific regulatory language.
Building the frameworks their firms will actually use.
Reporting AI risk to the board without hand-waving.
Moving from law, policy, or audit into AI governance.
Who must answer for what their models do.
The premise, the audience, and what you’ll be able to do by the end.
A working definition you can defend in any meeting.
Six controls that keep training data auditable.
The one-page document that changes every AI conversation.
Beyond the buzzwords — what actually shows up in evidence.
Reconstructing where governance would have intervened.
When optimization becomes a compliance emergency.
The 40 terms that unlock every future conversation.
No. The book is written specifically for non-technical GRC professionals. If you can read a policy document, you can read this book.
It’s a permanent reference, edited like a book, not a video dump. And it maps to real audit workpaper decisions, not certification exam questions.
Digital in-browser reader, cloth-bound hardcover print, and Amazon Kindle.
Yes. Volume 1 launches now. Volume 2 (Models, Metrics & Model Risk) is scheduled for Q3.
Yes — contact us for organizational licensing and cohort reading programs.
