CFO’s AI Playbook: How to use AI safely without risking sensitive data
We built a cashflow tool with AI. No actuals. No risk.
If you've felt a twinge of panic about plugging your company’s actuals into ChatGPT, you're not alone. CFOs, controllers, and FP&A leaders everywhere are caught between excitement and fear.
Excitement about what AI can automate.
Fear of what it might expose.
And that fear isn’t irrational. Financial data is sacred. It’s not just rows in Excel. It’s payroll, strategy, board decisions, and competitive edge, all rolled into one.
You shouldn’t trust AI blindly.
Especially when it comes to interpreting financials. And you don’t ask ChatGPT to interpret numbers. Ask it to write code.
Code is testable.
Code is auditable.
Code is repeatable.
You can read it. Run it. Break it. Improve it. Ask ChatGPT to explain every line of logic. You get full transparency, not a black box. That’s how CFOs can build internal tools without exposing sensitive data or relying on vague AI guesses.
AI is powerful, but trust is low. And in finance, trust is everything.
So while caution is wise, paralysis is costly. AI is here. It’s reshaping workflows, strategy, and expectations. And your competitors are already building with it.
In today’s edition, you’ll learn:
How to use AI safely.
How to build a cashflow reconcillation tool with an LLM
How to learn without leaking.
How to win without regret.
Let’s dive in.
Why Our Last App Was Safe (And Yours Can Be Too)
In our last edition, we shared how CFOs can build a forecasting app in 5 minutes.
The feedback was great but few of you raised concerns:
This looks cool, but is it safe to upload our real data?
How can you trust what comes out of an LLM?
But that app was designed to be safe.
We didn’t upload sensitive data.
It was a working prototype, built with the same paranoia most CFOs have about confidentiality. And it’s how you can start, too.
You can follow the same playbook. Use fictional data to test real-world use cases. Validate AI workflows. Share them with IT and your team. Scale up later.
Think of dummy data as a wind tunnel.
You can test without flying blind.
You can simulate real pressures, observe reactions, and tweak designs.
All before anything touches live data.