Proprietary business methods and processes, customized software, product specifications, sales information and sensitive marketing data and other confidential business information and methodologies are often among a company's most valuable assets. Unless these assets are proactively protected as "trade secrets," however, they can be lost forever thus jeopardizing a company's competitive advantage.
In order to establish trade secret protection, a company must show, among other things, that it has instituted reasonable safeguards to protect such know-how, processes and methodologies. Our Technology and Intellectual Property attorneys have extensive experience in designing and instituting such safeguards and maximizing our clients' trade secrets through a variety of measures, including by:
Identifying all relevant trade secret assets
Developing restrictive "need to know" access and use policies
Implementing physical safeguards, such as document numbering systems and encryption technologies
Deploying check in/check out procedures
Designing email and other electronic communication policies
Employing employee innovation, invention and other protective agreements
Using third-party confidentiality and other restrictive agreements
Considering and implementing other legal safeguards, such as patent protection
Additionally, in the event a client is either confronted with the misappropriation of its trade secrets or an allegation that it has misappropriated the trade secrets of another, our Technology and Intellectual Property litigators have an established track record for zealously and successfully representing our clients.
Imagine buying a medical-device company whose financial projections depend on manufacturing overseas. The company has an exclusive university patent license, promising test results and a government-supported research history. During diligence, the buyer discovers that the license covers the first product but not the next indication, the testing agreement limits access to supporting data, and the manufacturing plan needs a separate legal review.
Quantum computing poses an unusual problem for general counsel: the deadline is uncertain, but waiting for certainty may itself create material risk.
No publicly known quantum computer can presently defeat the public-key cryptography on which modern commerce depends. The timing of a “cryptographically relevant” quantum computer remains contested. Yet adversaries do not need to wait. They can collect encrypted information today and attempt to decrypt it later—a strategy commonly called “harvest now, decrypt later.” At the same time, large organizations may need years to locate cryptography embedded across applications, cloud services, connected products, operational technology, certificates, digital signatures, and third-party platforms.
By late 2024, Google was reporting that more than a quarter of its new code was being written by AI. Microsoft followed with similar numbers in 2025. In startups built from scratch on coding agents, the human share of the codebase can be a rounding error. The productivity story is familiar. The ownership story is not.
Not long ago, a technologist ran an experiment that should keep every software executive awake at night: using an off-the-shelf AI coding agent, he built a working Salesforce-style CRM — data model, interface and all — in roughly three hours. Not a mockup. A functioning product that took the original companies years and hundreds of millions of dollars to develop.
A model that summarizes internal meetings may present familiar privacy, security and contract questions. Connect the same model to a hiring score, credit decision, insurance recommendation or patient interaction, and the analysis changes. Put it in a public chatbot used by minors, and a different set of concerns appears. Use it to generate images or audio, and disclosure and provenance rules may matter.
This is the most useful way to read the emerging state AI patchwork: the legal unit of analysis is not the model. It is the moment when the system interacts with a person, influences a decision or produces an output that the law treats differently.
Acquirers are increasingly looking at businesses whose products use AI, depend on AI, or sit close enough to AI that the buyer expects future value from data, automation, software or model-enabled workflows. The target may not be an “AI company” in the headline sense. It may be a medical-device company using machine-learning outputs in a diagnostic workflow, a software platform embedding generative AI in a user interface, a manufacturer using computer vision in quality control, a services business with proprietary datasets and automation, or a consumer brand relying on AI-created marketing assets.
