Navigating Intellectual Property Protection in the Evolving AI Landscape
Written by Ben Esplin
Artificial intelligence, particularly generative models, has introduced profound complexity into the technical and legal ecosystems of technology development. Development and deployment of modern AI, especially large language models (LLMs), involves a variety of different aspects, each of which may be thought of as a separate technical expertise, and many of which are fertile ground for innovation. Some of these include pre-trained model architectures, fine-tuning or further training, model maintenance (e.g., protection from malicious hacking, model drift, etc.), and increasingly ingenious methods for integrating these models into existing products or systems. Critically, a person or team may demonstrate deep expertise in one aspect, such as prompt engineering or infrastructure deployment, while remaining relatively uninformed about others, like the underlying mechanics of the model. This layered sophistication often blurs the boundaries between what constitutes a protectable invention versus a functional application of shared technology.
A recurring challenge is the industry’s evolving vocabulary—terms like “AI,” “agent,” “wrapper,” and “model” are often used loosely, sometimes interchangeably. In reality, these terms represent distinct technological constructs. For instance, a "wrapper" is a technical artifact that mediates interaction between an external party and the core model, typically exposing a higher-level interface and facilitating integration with other systems. An "agent" refers to a system that leverages models, often orchestrated through prompt engineering to achieve specialized outcomes. The distinction matters, as the scope and nature of IP protection—whether copyright, trade secret, or patent—may depend on precise technical definitions.
Mislabeling technological constructs (e.g., conflating agents with wrappers) impedes clear legal strategies. Analogously, calling a complex system like a videogame a “query language” oversimplifies the technical ingenuity worthy of legal protection. The more granular the understanding of how a model is deployed, maintained, and integrated, the more robust the strategy for securing intellectual property rights.
The following are some exemplary aspects of AI development and deployment, and some broad discussion of IP protection strategy for each:
Model Development: Protecting proprietary model architectures and training pipelines through patents, and retaining trade secret protection for novel data curation or fine-tuning strategies.
Model Maintenance: Safeguarding bespoke maintenance workflows and security hardening techniques, especially those that confer operational advantages, as trade secrets.
Infrastructure & Tooling: Recognizing that the tools and frameworks enabling efficient scaling, training, and monitoring of models can themselves be valuable IP and may warrant patent, copyright, or trade secret protection.
Integration into Technology Platforms: Understanding the unique value in middleware, orchestrators, and other integration code that allows AI models to be effectively deployed within broader technology ecosystems. These may be patentable inventions or, at minimum, key differentiators to be protected by confidentiality agreements.
AI’s rapid evolution—its “word soup” and frequent conceptual misalignment—demands both technical literacy and legal precision. To protect innovation in model development, maintenance, infrastructure, and integration, it is imperative to adopt clear technical definitions and layered protection strategies. A nuanced approach ensures that clients not only secure their IP rights today, but that they retain flexibility and leverage as AI’s legal landscape continues to mature.
