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.
Shadow AI
Shadow AI refers to the unapproved, low-visibility use of generative tools across the business.
In many organizations, the highest-risk generative AI use is not the tool that has been vetted by IT, procurement or legal, it is the tool no one sees. Engineers may use personal accounts to refine invention disclosures, product teams may paste customer feedback into public models for summaries, in-house counsel may test arguments in unsanctioned systems and business leads may upload draft decks for "tone" edits. This unmanaged use is often referred to as "shadow AI."
Shadow AI typically reflects convenience, not bad intent. (see our detailed blog on Shadow AI in the Workplace: Hiding in the Shadows: The Perils of Shadow AI on Your Organization) The problem is that repeated, small disclosures can amount to a material loss of control over confidential information. Prompts, drafts, revisions and copied fragments can create a cumulative record, often across multiple vendors and accounts, of what was shared, when and under whose terms.
Against the backdrop of Heppner, unmanaged AI use is not merely an IT or policy issue, it is a rights preservation issue. Where employees submit legal theories, invention narratives, prototype details, competitive analyses or source materials to tools with unclear confidentiality commitments, the company may be weakening trade secret protection, creating privilege risk and complicating future enforcement. If a dispute arises later, the record may show that sensitive information moved through third-party systems under terms the business did not review or approve.
Shadow AI also shifts sensitive disclosure decisions to individual users. The people best positioned to assess legal sensitivity, namely R&D leads, patent counsel and security, are bypassed, and confidentiality calls get made “one prompt at a time.” The result is fragmented decision making and, often, uncontrolled data sprawl.
This is particularly corrosive in innovation heavy businesses because the most valuable information often appears ordinary to non-specialists. A prompt containing "just a few feature notes" may actually describe the core inventive step or a request to "make this easier to explain" may strip away the very technical distinctions that support patentability.
For most organizations, the solution is not a blanket ban. It is targeted controls for legally sensitive subject matter. If the organization cannot answer where AI is being used, by whom and on what terms, it cannot credibly claim it has controlled the flow of its confidential information.
Disclosure, however, is only part of the risk. AI can also reduce IP value by flattening what makes innovation technically and legally distinct. This quieter form of erosion is the focus of the next piece.
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 1: US v. Heppner: The End of “Just a Prompt” and Emerging IP 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.
