AI-powered FP&A vendors handle permissions and governance for sensitive financial data in four main ways: access controls that decide who sees what, logging and audit trails that record what AI and people did, approval steps that keep humans in charge of reported figures, and data-handling policies that say how your information is used. Vendors differ widely on all four, and your forecasts, margins and headcount plans are exactly the information you cannot afford to get wrong.
This guide gives you the questions to ask, what a strong answer sounds like, and a scorecard you can use to compare vendors before you spend time on features.
Key takeaways
Ask governance questions first. They filter out vendors faster than any feature comparison.
AI should respect each user's permissions, wherever the question is asked.
Every AI action should be scoped and logged, and every output should trace to source.
Finance should approve every change to reported figures. AI proposes, people approve.
Why governance comes before features
Governance questions decide whether a platform can operate inside your risk framework at all. AI FP&A platforms hold revenue forecasts, margin data, headcount plans and board-level projections. When security or audit teams raise gaps late in an evaluation, the project loses weeks. Starting with governance also gives your CFO and audit committee a clear record of due diligence. For background, see our overview of finance data governance.
AI adds a second layer to govern. Governance has to hold at the data layer, where permissions and lineage live, and at the AI layer, where questions are asked and answers produced. A platform that governs one without the other leaves a gap.
Six groups of questions to ask every vendor
1. Access and permissions
Who can see, edit and approve data, and how granular are the controls?
When someone asks AI a question, does the answer respect that user's permissions?
When a person changes roles or leaves, how quickly do permissions change?
How are sign-in and user provisioning managed?
A strong answer: permissions are defined once at the data layer and apply everywhere, including AI.
2. AI actions and logging
Which AI actions are scoped, and what can each one change?
Is every AI action logged, and can finance review the log?
Can administrators limit which users or roles can use AI features?
A strong answer: every action is scoped and logged, and the log is available to finance.
3. Traceability and explainability
Can every AI output be traced back to its source data, assumptions and the action that produced it?
Can finance ask what drives a model, line by line?
A strong answer: a glass box, not a black box. Every output links back to its origin.
4. Human approval of reported figures
Can AI change a reported number on its own?
Where does approval happen, and who can approve?
A strong answer: agents propose, the planning engine calculates, and finance approves every change to reported figures.
5. Data handling
Is our data used to train models, for us or for other customers?
Where does data flow when AI features are used, and which third parties are involved?
What happens to our data if we leave?
A strong answer: a written policy, available before you sign.
6. Ownership and security documentation
Who administers governance day to day, finance or IT?
Can you share current third-party security documentation and tell us what it covers?
Who approves model changes after go-live?
A strong answer: a named owner for governance, with documentation you can hand to your security team.
Governance scorecard
Question area | What strong looks like | Red flag |
|---|---|---|
Permissions | One permission model across data and AI | AI can see more than the user can |
Logging | Every AI action scoped and logged | Logs only for human users |
Traceability | Every output traces to source | Answers with no lineage |
Approval | Finance approves every change to reported figures | AI can change reported numbers |
Data handling | Written policy on training and data flow | "We will get back to you" |
Ownership | Named governance owner | Governance sits nowhere |
How we approach governance at Abacum
We built Abacum as a governed data and model layer for AI to reason over, so governance holds at the data layer and the AI layer. Every AI action is scoped and logged. Every output links to its source data, assumptions and the action that produced it. When your team asks from Claude, ChatGPT or Abacum, the answer comes from your source of truth, within each user's permissions. Our agents propose, the planning engine calculates, and finance approves every change to reported figures.
Abacum is run by finance, with no added load on IT, and enterprise-grade security and governance come built in. We always welcome IT and your CTO to the conversation. Implementation runs six to nine weeks depending on data complexity, entity count and workflows.
Walk through your governance questions with us
Book a demo of our FP&A software. Bring a planning scenario, your input table layout and entity structure, and the reports leadership expects, and we'll build a model around them in the call.
Related reading: Abacum Intelligence and Best AI FP&A software in 2026.
