A Strategic Framework for AI Copyright Risk Management

Generative AI (GenAI) presents copyright challenges that existing corporate policies do not address. Input risks arise when copyrighted materials are used for training, fine-tuning, or retrieval-augmented generation without authorization. Output risks include potential infringement and copyrightability questions affecting asset protection. As litigation grows and regulatory frameworks diverge globally, in-house counsel needs practical tools to assess exposure and implement defensible governance.

This guide provides a practical approach to developing AI-specific copyright policies grounded in current law and emerging best practices. It addresses the human authorship requirement, vendor indemnification, jurisdictional variation (including EU text and data mining exceptions and transparency obligations), and the advantages of licensing over fair use reliance.

Key topics in this 12-page guide include:

• Input vs. output risks: Understanding where liability arises across the AI lifecycle

• The human authorship requirement and protecting AI-assisted work product

• Decision trees for evaluating specific AI use cases against policy guardrails

• Governance frameworks for implementing AI copyright policy across teams

• Licensing strategies that provide certainty beyond fair use defenses

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