AI Insights and Fast Reads
This AI Insights Quick Read series examines a growing risk for innovation-driven businesses: how routine generative AI use can weaken confidentiality, compromise legal review, and erode the value of intellectual property. Across four short pieces, we consider the issue through the lens of US v. Heppner, then turn to shadow AI, the dilution of innovation through AI-generated content and the implications for Canadian organizations.
US v Heppner
Many companies treat generative AI as another productivity layer rather than a legal event. An employee pastes a draft into a chatbot, asks for a summary, cleans up a memo or pressure-tests an argument. It felt informal, disposable, and harmless. Judge Rakoff’s bench ruling in United States v. Heppner should end that illusion.
In Heppner, the court ruled that documents a defendant created using a commercial generative AI tool and then sent to counsel were not protected by attorney-client privilege or the work product doctrine on the facts presented. The key point was not that AI is categorically incompatible with privilege. The point was more basic and more dangerous that if a user shares information with a third-party tool that does not maintain confidentiality, the law may treat that act as disclosure outside the privileged relationship. Furthermore, if materials were not prepared by or at the direction of counsel, work product protection may fail as well.
This holding matters far beyond white collar defense and is a warning to every innovation driven business. Patent strategy, invention harvesting, claim drafting, source code analysis, trade secret audits, licensing plans, competitive intelligence and internal investigation memos all depend on controlled confidentiality. Once confidential material is pushed into the wrong AI environment, the legal issue is no longer merely "Was the output useful?" The issue becomes "What did we just disclose, to whom, and on what terms?"
The conversation therefore shifts from AI efficiency to intellectual property loss. Intellectual property rights are often lost not through theft, but through careless disclosure. Patent strategy can be undermined by premature sharing, trade secrets can be destroyed, privilege can be waived and valuable know-how can be diluted into generic language.
The broader takeaway from Heppner is that using AI is not a neutral operational choice. Each prompt reflects a governance decision, and each upload makes an implicit call about confidentiality. Even a quick AI generated summary of a legal or technical issue can create a discoverable trail of what was shared. Companies that still treat consumer chatbots as casual brainstorming aids are working from an outdated and risky assumption. The real question is no longer whether AI saves time, it is whether the time saved is worth the legal position lost.
Heppner is not just a privilege case. It is an early signal that AI use can alter the legal status of information long before a formal dispute arises. In practice, however, the greater challenge is how that risk enters the business through everyday, low-visibility use. That is the problem of shadow AI.
Explore the Full AI Insights Quick Read Series
For a deeper look at how generative AI impacts confidentiality, privilege and intellectual property value, explore the full series:
- Part 2: Shadow AI: The Unmanaged Use That Creates Real IP Disclosure Risk
- Part 3: AI Generated “Slop” and Overreliance on Summaries: How IP Value Gets Diluted
- Part 4: US v. Heppner’s Lessons for Canada: AI Use, Confidentiality and IP Exposure
If you would like to learn more about the opportunities and risks associated with artificial intelligence, we invite you to contact the authors of this series or any member of our Artificial Intelligence group.
