The ABCs of AI IP: What Rights Does Your Company Have in AI-Generated Materials?

AI is reshaping how companies create—marketing copy, code, designs, product specs, even deal documents. But the legal questions underneath are getting harder: Who (if anyone) is the “author”? What does your company actually own? What happens when AI output resembles third-party content, pulls from licensed datasets, or is created through tools with terms you didn’t negotiate? For in-house teams, the goal is practical: capture value from AI-enabled work while reducing IP and contract risk before it shows up in a demand letter, diligence request, or product launch.

Join us on Tuesday, March 31, 2026 at 5:30 PM PT in Los Angeles as Steve Stein and Andrew Lux (Greenberg Glusker) break down the evolving IP landscape for AI-generated materials—ownership, authorship, licensing, and risk management—through an in-house lens. You’ll leave with concrete steps to strengthen policies, contracts, and workflows so your teams can use AI tools confidently and defensibly.

Key Takeaways:

• How current law treats ownership and authorship for AI-generated work—and what that means for your company’s rights
• The most common licensing and terms-of-use traps that can undermine IP claims or create downstream obligations
• Practical approaches to protecting trade secrets when employees use third-party AI tools
• How to manage risks around similarity, infringement, and training-data questions (without stopping innovation)
• Contract clauses and internal controls to strengthen IP allocation, usage rights, and auditability across vendors and business teams

Who Should Attend: This program is built for in-house legal professionals advising product, marketing, engineering, data, and procurement stakeholders on AI tool adoption and AI-enabled work product. It’s especially relevant for counsel responsible for IP strategy, commercial contracting, privacy/tech governance, or risk management who need clear guidance on what their organization can claim, what it should avoid, and how to operationalize AI policies that work in the real world.