Matthew Fornaro, P.A. was recently featured in The AI Journal in a comprehensive article on AI due diligence — one of the most significant emerging legal risks facing founders, investors, and growth companies today.

The article, written by AI Journal staff journalist Erika Balla, draws on Matthew Fornaro’s more than twenty years of experience handling business transaction law, contracts, and disputes to explain why AI due diligence is rapidly becoming part of serious transactional planning — and what companies need to address before a deal is already moving.

The Shift from Operational Risk to Transaction Risk

AI due diligence is different from operational AI risk. It asks what legal exposure exists before a transaction closes — before an investor funds the business, before an acquirer signs a letter of intent, before a material vendor relationship is finalized. Many businesses have adopted AI tools in ways that created exposure they have not fully documented, reviewed, or disclosed. Once a transaction starts, those issues become diligence questions, negotiation points, and sometimes post-closing problems.

Ownership of AI-Assisted Work Product

A work created entirely by AI without meaningful human authorship may not enjoy the same copyright protection as a work created and directed by a person. Companies should document where AI was used, preserve evidence of human direction and revision, and review the ownership chain early — before the issue is raised by an investor, buyer, or opposing counsel.

Vendor Terms and the Model Training Problem

Many companies do not realize that their AI vendors may claim broad rights over user inputs, improvement data, or model training materials. If a business has fed confidential business plans, customer information, internal documents, or proprietary methods into an AI tool, those terms matter significantly in any transaction context.

Customer Contracts and Hidden AI Conflicts

A company may have signed service agreements, NDAs, or master service agreements that limit how customer information can be processed or shared — then employees begin using AI tools in ways that create conflict with those existing commitments. That mismatch can sit quietly in the background until the wrong deal or the wrong discovery request brings it to the surface.

Governance, Audit Trails, and Internal Accountability

Internal AI governance should be treated more like data security, document retention, or financial controls. A business that cannot explain how an AI tool was used is in a much weaker position if a dispute arises. The companies that build governance infrastructure early will find diligence cleaner. Those without it will be trying to recreate order while a deal is already in motion.

AI Due Diligence Is Not Just a Technology Company Issue

This is the most important point: AI due diligence is not only for software companies or venture-backed platforms. It is now relevant to service businesses, healthcare-adjacent companies, e-commerce companies, professional firms, and any founder-led company that uses AI in even modest ways. If the business uses AI in customer service, sales support, document drafting, workflow automation, analytics, or internal operations, AI already sits somewhere in its risk profile.

This article was originally published in The AI Journal on July 3, 2026. Read the original here.