---
id: "framework-gen-ai-risk-mitigation"
type: "framework"
source_timestamps: ["§ Lessons for Gen AI Companies"]
tags: ["risk-management", "corporate-strategy", "compliance"]
related: ["claim-piracy-financial-risk", "action-build-opt-out", "concept-piracy-caveat"]
steps: ["\\\"Audit Training Data for Piracy: Assess the financial exposure generated by the use of 'shadow libraries' and pirated content", "calculating potential statutory damages.\\\"", "\\\"Sign Proactive Licenses: Capitalize on current rightsholder anxiety to negotiate favorable licensing deals for clean", "curated data.\\\"", "Develop Opt-Out Infrastructure: Build user-facing tools (similar to YouTube/Facebook content ID systems) that allow rightsholders to easily filter or remove their content from training datasets.", "\\\"Re-evaluate Unlicensed Data Necessity: Test models against fully licensed/open-source datasets (e.g.", "Common Pile v0.1) to determine if the marginal performance gain of unlicensed data justifies the legal risk.\\\""]
sources: ["tail2"]
sourceVaultSlug: "hbr-seg-tail2"
originDay: 2
articleStem: "hbr-tail-126-genai-copyright"
sourceUrl: "https://hbr.org/2025/07/can-gen-ai-and-copyright-coexist"
sourceTitle: "Can Gen AI and Copyright Coexist?"
---
# Gen AI Legal Risk Mitigation Strategy

A risk-mitigation framework for generative-AI companies, from [[entity-michael-d-smith]] and [[entity-rahul-telang]]. The authors warn against complacency after early, partially favorable rulings: the piracy caveat (see [[concept-piracy-caveat]]) means catastrophic exposure survives even a fair-use win.

**The four moves:**
1. **Audit training data for piracy** — quantify exposure from shadow-library reliance (see [[concept-shadow-libraries]]) and compute potential statutory damages under §504 (see [[claim-piracy-financial-risk]], [[prereq-statutory-damages]]).
2. **Sign proactive licenses** — capitalize on current rightsholder anxiety to negotiate favorable deals for clean, curated data (see [[concept-curated-training-datasets]]) while sellers are motivated.
3. **Develop opt-out infrastructure** — build user-facing tooling, akin to YouTube/Facebook Content ID, letting rightsholders filter or remove their content from training sets. → [[action-build-opt-out]].
4. **Re-evaluate unlicensed-data necessity** — empirically test models against fully licensed/open-source datasets such as Common Pile v0.1 (see [[claim-unlicensed-data-performance]]) to see whether the marginal gain from unlicensed data is worth the legal risk.

The throughline: reduce exposure to statutory damages, pivot from scraping to licensing, and challenge internal assumptions about what data is actually required. This is the AI-company-side mirror of [[framework-rightsholder-defense]].