N10-7: AI Regulatory & IP Risk Assessment
Evaluating intellectual property, regulatory exposure, and compliance risk during AI due diligence.
🎯 What You'll Learn
- ✓ Assess IP ownership
- ✓ Evaluate regulatory risk
- ✓ Identify litigation exposure
- ✓ Calculate compliance costs
Lesson 1: AI IP Ownership Analysis
Who owns the AI? If the model was trained using open-source frameworks (mostly fine), on third-party APIs (they own the outputs in many cases), by contractors (check the IP assignment clause), or using customer data (check the data processing agreement). Clean IP ownership is non-negotiable for acquisition.
Who owns the trained model weights? The company, the cloud provider, or the researcher?
Some API providers claim rights to outputs generated through their APIs.
Models trained on customer data may trigger data processing agreement restrictions.
Audit your AI IP ownership: training data rights, model weight ownership, output rights, and contractor IP assignments.
Lesson 2: Regulatory Risk Mapping
Map your AI product against the emerging regulatory landscape: EU AI Act (risk classification), US state-level AI laws (bias audits), sector-specific regulations (financial, healthcare, employment), and international data sovereignty requirements. Each regulation creates compliance costs and potential liability.
Classify your AI as minimal, limited, high, or unacceptable risk under the EU AI Act.
NYC Local Law 144 and similar laws require bias audits for employment AI.
Training on EU data and serving US customers (or vice versa) triggers sovereignty issues.
Map your AI product against 3 applicable regulations. Calculate the compliance cost and penalty exposure for each.
Lesson 3: Litigation Exposure Assessment
AI litigation is exploding: copyright suits, bias discrimination claims, privacy violations, and output liability. Evaluate: (1) Training data copyright claims (estimated litigation cost if sued), (2) Algorithmic bias exposure (discrimination lawsuits in employment, lending, or housing contexts), (3) Output liability (who is liable when the AI gives dangerous or incorrect advice?).
Estimated cost to defend a training data copyright claim: $500K-5M.
Discrimination lawsuits can reach class-action status with damages in the tens of millions.
If the AI provides incorrect medical, legal, or financial advice, who is liable?
Assess your AI product's litigation exposure across copyright, bias, and output liability. Estimate total legal risk.
Continue Learning: Track 10 — AI Due Diligence
2 more lessons with actionable playbooks, executive dashboards, and engineering architecture.
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Module Syllabus
Lesson 1: Lesson 1: AI IP Ownership Analysis
Who owns the AI? If the model was trained using open-source frameworks (mostly fine), on third-party APIs (they own the outputs in many cases), by contractors (check the IP assignment clause), or using customer data (check the data processing agreement). Clean IP ownership is non-negotiable for acquisition.
Lesson 2: Lesson 2: Regulatory Risk Mapping
Map your AI product against the emerging regulatory landscape: EU AI Act (risk classification), US state-level AI laws (bias audits), sector-specific regulations (financial, healthcare, employment), and international data sovereignty requirements. Each regulation creates compliance costs and potential liability.
Lesson 3: Lesson 3: Litigation Exposure Assessment
AI litigation is exploding: copyright suits, bias discrimination claims, privacy violations, and output liability. Evaluate: (1) Training data copyright claims (estimated litigation cost if sued), (2) Algorithmic bias exposure (discrimination lawsuits in employment, lending, or housing contexts), (3) Output liability (who is liable when the AI gives dangerous or incorrect advice?).