Part 2: Due Diligence in AI Startups: The Legal & Ethical Red Flags Investors & Founders Can't Ignore.
n 2025, legal and ethical due diligence isn't just a checkbox—it's a gating function. And for AI startups, it's far more complex than most founders realise.
A few months ago, a founder shared a story with me that I still think about.
"Our AI was outperforming everything else in the market but we still couldn't close the round. The VCs loved the product. Then the legal team got involved."
The deal didn't fall apart due to the technology, market opportunity, or metrics. It stalled because of one simple yet critical question:
"Who owns the data this was trained on?"
That single question changed everything.
In 2025, legal and ethical due diligence isn't just a checkbox—it's a gating function. And for AI startups, it's far more complex than most founders realise. Investors are not just evaluating your product's performance, but also what lies beneath it, such as your data sources, compliance frameworks, and ability to navigate a rapidly evolving regulatory landscape[1][4].
For founders, this shift represents both a challenge and an opportunity. Those who prepare thoroughly can turn diligence into a competitive advantage, inspiring confidence and closing rounds faster. Those who don't? They risk losing trust and dealing in an instant[3][6].
Why Legal Due Diligence in AI Is Different
Let's start with what most founders know: traditional startup due diligence is relatively straightforward. Investors want clean cap tables, properly executed contracts, clear IP assignments, and basic corporate hygiene.
However, AI startups are a different breed entirely. When you build AI systems, you're not just selling code but creating technologies that learn, adapt, and make decisions based on real-world data. These systems interact with people and environments in ways that are often opaque and unpredictable[1][4][8].
This complexity introduces a new layer of risk and a new set of questions from investors:
Who owns the algorithm? Did your team build it from scratch, or does it rely on open-source components?
Where did the training data come from? Was it licensed? Scraped? Provided by customers?
What are the ethical risks? Could your model be biased or discriminatory?
Are you compliant with privacy laws like GDPR?
Does your technology fall under national security regulations?
These aren't theoretical concerns; they're real issues that can derail deals if left unaddressed[2][4][6]. But here's the good news: with the right preparation, these challenges are navigable. Let's break down six of the biggest red flags investors look for and how you can address them head-on.
1. Who Owns the AI? (IP Rights & Data Moats)
This is always where due diligence begins and where things often unravel. Intellectual property (IP) ownership is foundational to any tech investment, but it's especially critical in AI startups, where proprietary algorithms and data moats are key drivers of value[1][4].
Investors will want clear answers to questions like:
Is your algorithm proprietary or open-source?
Were all contributors employees, contractors, and collaborators—properly assigned IP rights?
Do you own the outputs generated by your model?
What rights do you have to the training data?
For example, under UK copyright law, AI-generated content is only protected if an identifiable party is responsible for its creation. Similarly, patent protection requires specific technical contributions that must be well-documented[1][6]. Without clarity on these points, investors may see your IP as vulnerable or, worse, legally indefensible.
Founder Tip:
Have every contractor and contributor sign airtight IP assignment agreements from day one. Maintain detailed version history and architecture documentation for your algorithms. Keep meticulous records of every dataset you use, including its source, licensing details, and any restrictions on its use[1][4].
2. Is the AI GDPR compliant? (Data Usage & Privacy Law)
Data privacy laws, such as GDPR, are among the most common hidden landmines in AI startups, and they're not going away anytime soon. Many founders assume that publicly scraped data is "safe" to use for training models, but under GDPR, even publicly available content can qualify as personal data if it relates to an identifiable individual[2][4].
The risks don't stop there:
If you've trained models on customer data without explicit consent or suitable anonymisation protocols, you could violate GDPR[2].
If your product captures sensitive user data, such as biometrics or behavioural analytics, you may be subject to even more stringent legal obligations[4][6].
Founder Tip:
Conduct regular Data Protection Impact Assessments (DPIAs) to identify and mitigate privacy risks early on. Anonymise or pseudonymise datasets wherever possible to reduce exposure. Build opt-in consent mechanisms into your product from day one; it's easier than retrofitting compliance later[2][4].
3. The NSIA Trap: Is Your Tech a National Security Risk?
This one catches many founders off, especially those operating in sensitive sectors like defence or surveillance technologies. Under the UK's National Security and Investment Act (NSIA), certain types of AI fall under "sensitive sectors" that require pre-approval for fundraising or M&A activity[1][3].
If your technology can:
Detect or track objects or individuals
Control autonomous physical systems (e.g., drones or vehicles)
Interface with critical national infrastructure
…you may need government clearance before proceeding with certain transactions[1][3][6].
This process can delay deals—or even block them altogether if not handled proactively.
Founder Tip:
If your technology operates in regulated domains, consult legal experts familiar with NSIA compliance early in your fundraising process. Include documentation in your data room explaining how your product aligns with or avoids regulated uses[1][3].
Useful Checklist
Here’s a practical checklist to help you build trust during due diligence:
✅ Confirm IP assignments for all contributors
✅ Catalogue your data sources and licenses
✅ Document GDPR compliance and user consent
✅ Check if you fall under NSIA or sector-specific regulation
✅ Create an ethics log: bias testing, explainability, safeguards
✅ Audit your open-source stack and clean up risky licenses
✅ Include liability frameworks in your product documentation
Final Thoughts
Here’s the thing no one tells you early on:
Diligence doesn’t kill deals—surprises do.
In AI, your greatest strength may be your technical expertise. However, your greatest risk is what’s hiding underneath it. Therefore, Founders who take legal and ethical diligence seriously don’t just avoid delays.
They inspire confidence.
They close faster.
They earn more trust.
And they send a powerful signal to the market:
We’re not just building fast—we’re building right.
I hope you enjoyed this article.
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References and Further Reading
[1] AI: Conducting legal, due diligence – Law services | EY - Australia https://www.ey.com/en_au/insights/law/ai-conducting-legal-due-diligence-law-services
[2] AI Compliance: A Must-Read for Fintechs Using AI - InnReg https://www.innreg.com/blog/ai-compliance-a-must-read-for-fintechs-using-ai
[3] 5 Essential Due Diligence Red Flags That AI Can Help Uncover https://www.linkedin.com/pulse/5-essential-due-diligence-red-flags-ai-can-help-uncover-skylarkai-mz01e
[4] AI Due Diligence: Key Steps, Best Practices & Checklist [GUIDE] https://redblink.com/ai-due-diligence/
[5] [PDF] Compliance Costs of AI Technology Commercialization - arXiv https://arxiv.org/pdf/2301.13454.pdf
[6] Spotting Red Flags in Due Diligence | AI-Powered Risk Detection https://www.aracor.ai/blog/spotting-red-flags-in-due-diligence
[7] [PDF] An Analysis of AI Integration in the Context of Mergers and ... https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=9155896&fileOId=9155897
[8] AI Due Diligence: What It Is and How It's Changing M&A - Grata https://grata.com/resources/ai-due-diligenc