Can You Enforce Trade Secrets in “Vibe-Coded” Systems?

AI-Assisted Development Is Reshaping Software Ownership, Confidentiality and Litigation Risk

Artificial intelligence (AI) is rapidly transforming how software is developed. More than ever, engineers are no longer writing every line of code manually. Instead, they are building systems through iterative prompting, AI-assisted debugging, conversational development workflows and automated code generation—a phenomenon colloquially referred to as “vibe coding.”

The efficiency gains are substantial. Companies can prototype faster, reduce development costs and enable smaller teams to build increasingly sophisticated products. But these new AI-assisted workflows are also creating difficult and largely unresolved questions concerning trade secret protection, ownership and litigation risk.

A critical issue is emerging: can companies meaningfully enforce trade secret rights in systems that were partially generated, refined or operationalized through AI tools? The answer is likely yes, but only if companies rethink how they identify and protect proprietary information in AI-enabled environments.

The Trade Secret Puzzle Is Changing

Traditionally, software disputes center on relatively identifiable assets such as source code repositories, technical specifications, copied files and other company-owned data. In AI-assisted development environments, however, the most valuable information may no longer reside in a single body of code. Competitive advantage may instead arise from proprietary prompting strategies, workflow orchestration, optimization methodologies, internal integrations, deployment pipelines and accumulated engineering judgment. In many cases, the “secret sauce” is not the output itself, but the system of interactions between human expertise and AI-generated assistance that produced the final product.

This evolution creates challenges for trade secret enforcement. Applicable law generally requires companies to identify protectable confidential information and demonstrate that they took reasonable measures to preserve its secrecy. That framework becomes more difficult to apply when development occurs conversationally and iteratively through AI systems. Courts may increasingly confront disputes in which the alleged trade secret is not a specific block of copied code, but an evolving process involving prompts, workflows, model configurations and engineering methodologies.

The Evidentiary Challenges Ahead

AI-assisted development also creates new evidentiary complications. Multiple developers using the same foundation models may independently generate similar solutions. At the same time, organizations may have limited visibility into how AI systems process prompts, retain contextual information or influence future outputs. As a result, future litigation may focus less on literal copying and more on whether AI-enabled workflows replicated proprietary technical judgment, institutional knowledge or operational methodologies in ways that trade secret law can meaningfully address.

The risks become even more complicated because many organizations cannot yet fully document:

• how AI-assisted systems were developed;
• what prompts or workflows were used;
• whether proprietary information entered external systems; or
• how independent development can be demonstrated if disputes arise.

Companies that lack clear governance and documentation may face significant disadvantages during litigation, internal investigations, regulatory inquiries or transactional diligence.

Employee Mobility and “Tacit Knowledge” Risks

The risks are particularly acute when developers move between companies. Historically, software disputes often involved allegations that employees copied repositories or retained confidential documents. In AI-assisted environments, however, an employee may no longer need to physically remove files to create litigation risk. A developer familiar with a prior employer’s systems may be able to reconstruct similar workflows, architectures, or optimization strategies using AI-assisted prompting and generalized technical guidance. This creates difficult questions concerning independent development and tacit knowledge. Companies may increasingly argue that former employees improperly transferred institutional expertise through AI-assisted replication, even where no direct copying occurred. At the same time, employers seeking to hire top AI talent must balance innovation objectives against growing trade secret exposure. These disputes are likely to become especially significant in industries where competitive advantage depends heavily on proprietary workflows, training methodologies, or operational know-how rather than solely on patent portfolios.

The Growing Risk of AI “Contamination”

The use of external AI coding tools also raises concerns regarding contamination and confidentiality. Developers frequently submit snippets of code, debugging materials, architectural descriptions, or operational workflows into AI systems. Even where vendors limit model training or retention, organizations may still face uncertainty concerning how proprietary information is processed, stored, or surfaced through future outputs. These concerns extend beyond litigation risk. Investors, acquirers, regulators, and counterparties are increasingly asking whether organizations can adequately explain how critical systems were developed and whether proprietary information was exposed to external models.

For many organizations, existing confidentiality and cybersecurity policies were not designed for AI-enabled development environments. As a result, governance structures may lag significantly behind actual employee practices.

What Companies Should Be Doing Now

Organizations should begin treating AI-assisted development governance as both a technical and legal priority. Existing trade secret policies, cybersecurity procedures, and confidentiality agreements should be reevaluated to address prompt-based development workflows and AI-enabled coding systems directly. Companies should consider clearly identifying and documenting AI-related confidential assets, including proprietary workflows, orchestration systems, optimization methodologies, deployment practices, and internal prompting frameworks. Maintaining development lineage may also become increasingly important. Organizations that can demonstrate how systems were built, what tools were used, and how proprietary information was protected are likely to be in a significantly stronger position if disputes arise. Governance over employee use of AI coding tools is equally important. Organizations should establish clear rules concerning approved AI platforms, permissible prompt content, use of external versus internal models, and handling of sensitive source code or technical materials. Employee mobility procedures also warrant renewed attention. Onboarding, confidentiality, and offboarding protocols should address AI-assisted development risks directly, including restrictions on importing prior employer workflows, prompts, or proprietary methodologies into new environments. Finally, companies should carefully evaluate vendor agreements governing AI development platforms. Data retention practices, ownership provisions, model training restrictions, and cross-border processing issues may all become relevant in future disputes.

How Brownstein Can Help

As AI development accelerates, many organizations are discovering that traditional trade secret and technology governance frameworks no longer fully align with modern software development realities. Brownstein’s multidisciplinary team is positioned to help companies address these evolving risks from both strategic and operational perspectives. Brownstein can assist organizations in:

• evaluating AI-related trade secret exposure;

• developing AI governance and confidentiality frameworks;

• assessing employee mobility and onboarding risks;

• structuring defensible AI development policies;

• conducting internal investigations involving AI-assisted workflows;

• preparing for AI-related litigation and regulatory scrutiny; and

• coordinating cross-border risk management involving data, technology, and emerging AI regulations.

In addition to litigation readiness, Brownstein’s integrated government affairs and policy capabilities allow the firm to help clients monitor and anticipate rapidly evolving legislative and regulatory developments affecting AI, trade secrets, data governance, and emerging technology industries.

Looking Ahead

AI-assisted software development is likely to become a permanent feature of the innovation economy. But as “vibe coding” accelerates, traditional assumptions about software ownership, confidentiality, and trade secret protection are beginning to erode. The next generation of litigation may not focus primarily on copied code, but on whether AI-enabled systems reproduced proprietary workflows, engineering judgment, or institutional know-how in ways that existing legal frameworks struggle to address. Organizations that proactively establish governance structures, preserve development history, and rethink how they define and protect confidential technical information will likely be far better positioned as this emerging area of litigation continues to develop.

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This document is intended to provide you with general information regarding AI and trade secret risk. The contents of this document are not intended to provide specific legal advice. If you have any questions about the contents of this document or if you need legal advice as to an issue, please contact the attorneys listed or your regular Brownstein Hyatt Farber Schreck, LLP attorney. This communication may be considered advertising in some jurisdictions.