AI Due Diligence for Fundraising: Training Data, Model Risk and Output IP

TL;DR

• Investors may investigate where data comes from, what rights the company has and whether third-party models create concentration risk.
• Document what the company owns versus licenses or consumes through APIs.
• Be precise about output rights, privacy, security and model limitations.
• Connect technical risks to commercial impact instead of treating them as a separate compliance exercise.

The major AI diligence workstreams

AI diligence is not only a model-quality review. The investor wants to know whether the product can continue operating, serving customers and defending value if model pricing, regulation, data availability or security conditions change.

Practical workflow

1. Data provenance

Map training, fine-tuning, retrieval and evaluation data sources.

2. Model dependencies

List third-party model providers, versions, fallback options and material contract constraints.

3. IP ownership

Document code, model improvements, datasets, licenses and employee/contractor assignments.

4. Output and customer risk

Explain how outputs are used, reviewed and constrained in the product workflow.

5. Security and privacy

Show how sensitive data is handled and access is controlled.

6. Governance

Document evaluation, incident response and risk ownership appropriate to the product.

What to prepare

• Data-source register.
• Model/provider dependency map.
• IP and license records.
• Privacy/security documentation.
• Evaluation and incident processes.
• Customer terms relevant to AI outputs.

Related SendNow resource: AI startup fundraising workflow.

Common mistakes

• Claiming ownership over third-party model capabilities.
• Using training or retrieval data without clear rights.
• Ignoring provider concentration risk.
• Treating hallucination or output risk as only a product issue rather than a commercial one.

External reference: NIST AI Risk Management Framework. This article is educational and not legal, regulatory or financial advice.

See the VDR and Microsite workflow

Frequently asked questions

Do investors need the raw training data?

Usually they need evidence about provenance, rights and process rather than unrestricted access to raw data.

Why do model dependencies matter?

Pricing, availability, terms or model changes can affect product performance and economics.

What is output IP risk?

It includes uncertainty around ownership, licensing or infringement issues connected to generated outputs and the way customers use them.

Build a cleaner AI investor workflow

Use SendNow Microsites when AI fundraising moves beyond the deck into multi-file diligence.