What to Disclose About AI Training Data and Methods in M&A: Avoiding Legal and Compliance Pitfalls

As artificial intelligence becomes a core differentiator in software and SaaS valuations, acquirers are scrutinizing not just the performance of AI models, but the provenance of the data and methods used to train them. For founders and CEOs preparing for a strategic exit, the question is no longer whether to disclose details about your AI stack — it’s how much, how early, and how clearly.

In this article, we outline the key disclosures around AI training data and methodologies that can help avoid post-transaction surprises, regulatory exposure, or valuation haircuts. Whether you’re fielding interest from a strategic buyer or preparing for a private equity diligence process, these insights can help you stay ahead of the curve.

Why AI Training Data Is a Due Diligence Flashpoint

AI systems are only as good — and as safe — as the data they’re trained on. In recent years, lawsuits and regulatory actions have highlighted the risks of using copyrighted, biased, or personally identifiable data in model training. Acquirers, especially those with public market exposure or global operations, are increasingly wary of inheriting these liabilities.

For example, in 2023, several generative AI companies faced class-action lawsuits over the use of copyrighted content scraped from the web. Meanwhile, the EU’s AI Act and similar frameworks in the U.S. and Asia are introducing stricter transparency and data governance requirements. In this environment, opaque or undocumented AI training practices can become deal-breakers.

Key AI-Related Disclosures to Prepare

To ensure a smooth diligence process and preserve deal value, sellers should be prepared to disclose the following:

1. Source and Licensing of Training Data

• Was the data collected in-house, licensed from third parties, or scraped from public sources?
• Do you have documentation of data licenses, terms of use, or consent agreements?
• Have you used any datasets that may include copyrighted material, personal data, or proprietary content?

Buyers will want to see a clear chain of custody and legal basis for data usage. If your models were trained on open-source datasets, be ready to explain the license terms (e.g., Creative Commons, Apache 2.0) and any restrictions they impose.

2. Data Governance and Privacy Compliance

• Have you implemented data minimization, anonymization, or differential privacy techniques?
• Are your data practices compliant with GDPR, CCPA, or other relevant privacy laws?
• Do you maintain audit logs or documentation of data handling procedures?

Especially for companies operating in or selling to the EU, demonstrating compliance with the EU AI Act and GDPR is critical. Acquirers may request a third-party audit or legal opinion to validate your practices.

3. Model Training Methodology and Documentation

• What algorithms, frameworks, and infrastructure were used to train your models?
• Do you maintain version control, reproducibility logs, or model cards?
• Have you conducted bias testing, explainability assessments, or adversarial robustness checks?

Buyers are increasingly interested in the governance of your AI development process — not just the outcomes. Well-documented training pipelines and model evaluation protocols can increase buyer confidence and reduce the need for post-close remediation.

4. Third-Party Dependencies and Open Source Use

• Are you using any third-party AI models (e.g., OpenAI, Hugging Face) or APIs?
• Have you reviewed the terms of service and usage restrictions for these tools?
• Do you rely on open-source libraries with copyleft licenses (e.g., GPL) that could impose obligations on derivative works?

As we’ve seen in Legal Documents Required to Sell a SaaS Business, open-source usage can trigger complex IP questions. In the AI context, these risks are magnified if your product incorporates or builds upon third-party models without clear licensing terms.

Strategic Implications for Valuation and Deal Structuring

From a buyer’s perspective, unclear or risky AI data practices can lead to:

• Lower valuation multiples due to perceived compliance risk
• Holdbacks or indemnity escrows to cover potential liabilities
• Delayed closings while legal teams investigate data provenance

Conversely, companies that proactively address these issues — and can demonstrate a robust AI governance framework — may command a premium. At iMerge, we’ve seen firsthand how early preparation in this area can accelerate diligence and preserve deal momentum.

In one recent transaction, a mid-market SaaS firm with a proprietary AI recommendation engine was able to justify a 20% valuation premium by providing detailed documentation of its training data sources, licensing agreements, and model audit logs. The buyer, a publicly traded strategic acquirer, cited this transparency as a key factor in their decision to move forward without a material indemnity holdback.

How to Prepare: A Practical Checklist

To get ahead of buyer concerns, consider assembling the following materials before entering the M&A process:

• Data source inventory with licensing and consent documentation
• AI model documentation (training methods, evaluation metrics, version history)
• Privacy compliance reports or third-party audits
• Open-source software inventory with license types and usage notes
• Internal policies on AI ethics, bias mitigation, and data governance

These materials can be included in your data room or summarized in your Confidential Information Memorandum (CIM) to preempt buyer questions and demonstrate operational maturity.

Final Thoughts

AI is no longer a black box that buyers are willing to take on faith. As regulatory scrutiny intensifies and legal precedents evolve, the burden of proof is shifting to sellers. By proactively disclosing your AI training data sources, licensing practices, and model governance protocols, you not only reduce legal risk — you also position your company as a credible, acquisition-ready asset.

Firms like iMerge specialize in helping software and AI-driven companies navigate these complexities, from pre-LOI positioning to post-close integration planning. Whether you’re preparing for a strategic exit or exploring growth capital, early alignment on AI disclosures can make all the difference.

Founders navigating valuation or deal structuring decisions can benefit from iMerge’s experience in software and tech exits — reach out for guidance tailored to your situation.