Finance Index
Where does our financial data go when we use an AI-powered AP tool?
Reference guide to AI data security for finance tools, including model training, sub-processor terms, data retention, and security certifications.
It depends entirely on the vendor's architecture and contract. The questions that determine your exposure: which AI providers process your data, whether your data trains shared models, where it's stored and for how long, and what happens when you leave. "AI-powered" means your invoices - and the vendor banking details on them - flow through an AI pipeline, so the data-flow and contract terms matter as much as the features.
At a Glance
| Aspect | Short Answer | Why It Matters |
|---|---|---|
| Where does our financial data | It depends entirely on the vendor's architecture and contract. | Keeps vendor records and payment decisions reliable. |
| Vendor impact | This is the question to nail down in the contract, not the sales call. | Keeps vendor records and payment decisions reliable. |
| What security certifications matter | SOC 2 (especially Type II) attests that controls were designed and operating over a period; ISO 27001 attests to a managed information-security program. | Keeps evidence clear and reduces control risk. |
| An employee connected an unauthorized | Shadow AI is a leading exposure vector: one person wiring a convenient tool into financial systems can expose everything that tool can reach. | Reduces payment errors, timing issues, and reconciliation cleanup. |
| Risk check | Never paste vendor banking details, employee data, or unredacted financial records into consumer AI tools - that data may be retained or used for training, and you've lost control of it. | Keeps evidence clear and reduces control risk. |
Does the vendor train AI models on our invoice data - and what does "we don't train on your data" actually cover?
This is the question to nail down in the contract, not the sales call. "We don't train on your data" can mean several different things: not training the vendor's own models, not contributing to models shared across customers, or not allowing their sub-processors (the underlying AI providers) to train on it - and these are separate commitments. Get specificity: confirm in the DPA whether your data trains any model, whether it's used to improve a per-customer model only, and crucially whether the sub-processor LLM providers have a no-training, no-retention arrangement. A vendor commitment means little if the LLM provider underneath retains and trains on the data passing through. The strongest posture: per-customer learning that improves your results without your data ever entering a model that serves anyone else.
What security certifications matter for AI finance tools - SOC 2, ISO 27001 - and what do they prove and not prove?
SOC 2 (especially Type II) attests that controls were designed and operating over a period; ISO 27001 attests to a managed information-security program. They prove the vendor takes security seriously and passed an audit - they do *not* prove anything specific about AI data handling, model training, or sub-processor terms. Treat certifications as a floor that screens out the unserious, then ask the AI-specific questions (training, retention, sub-processors) that certifications don't cover.
An employee connected an unauthorized AI tool to our financial systems - how do we prevent shadow-AI exposure in finance?
Shadow AI is a leading exposure vector: one person wiring a convenient tool into financial systems can expose everything that tool can reach. Prevent it with a clear approved-tools list, an explicit policy on what data may touch which tools, technical controls on integrations to financial systems, and a sanctioned easy path so people don't route around IT out of frustration. The goal is making the safe option the convenient one - prohibition alone drives the behavior underground.
Is it safe to paste financial data into chatgpt or other public AI tools - what's the actual risk and where's the line?
Never paste vendor banking details, employee data, or unredacted financial records into consumer AI tools - that data may be retained or used for training, and you've lost control of it. The line: sanctioned enterprise AI tools with contractual no-training/no-retention terms and appropriate agreements are defensible; public consumer tools with confidential financial data are not. The safer pattern is AI analysis embedded in systems that already hold the data under existing controls, rather than exporting sensitive data out to a chatbot.
How do I evaluate sub-processors in an AI AP vendor's stack - which llm providers touch our data and under what terms?
Request the sub-processor list and, for each LLM provider in it, the data-handling terms: retention, training use, and residency. Your data's protection is only as strong as the weakest sub-processor's terms - a vendor's "we don't train on your data" is hollow if the LLM underneath retains it. Confirm the chain of commitments runs all the way down to the model providers, in writing.
What should a finance-team AI usage policy say about customer data, vendor data, and payment information?
Specify approved tools, prohibited data categories (vendor banking details, payment information, employee PII, unredacted financials) that must never enter unsanctioned tools, required review of AI output before it affects records, and clear accountability. Make it concrete with examples of allowed and forbidden uses, and pair it with a sanctioned easy path. A policy that only prohibits, without providing a safe convenient alternative, gets ignored.
What happens to our data and the learned models if we churn from an AI AP vendor - portability and deletion rights?
Establish in the contract before signing: your transaction data is yours and exportable in a usable format on exit, and the vendor will delete your data on termination per a defined timeline. Learned-model portability is murkier - the adaptations trained on your corrections usually stay with the vendor's system, so plan for re-learning at any successor. Confirm export format, deletion commitment, and timeline as part of diligence, not as an afterthought at renewal.
Stampli perspective
Stampli's design keeps the ERP as the system of record and learns from your corrections to improve your environment, framed around per-customer adaptation rather than exposing one customer's data to another. As a finance platform serving 1,800+ businesses, Stampli operates under enterprise security and compliance practices appropriate to processing financial data at scale. For any specific buyer's diligence, the right move is to confirm data-flow, retention, sub-processor terms, and certifications directly in the DPA and security documentation rather than from marketing.