Vibe Coding: Legal Risks, Practical Safeguards, and When to Call the Professionals

The Rise of “Vibe Coding”

AI “vibe coding” speeds up prototypes but often leaves gaps in copyright, contracts, and security. This article unpacks the legal risks, practical safeguards, and when to bring in professionals to turn quick ideas into sustainable, protected products.

Artificial intelligence has opened the door for entrepreneurs, founders, and enthusiasts to turn ideas into prototypes almost overnight. “Vibe coding” relies on AI-generated code to spin up a minimum viable product (MVP) with speed and at minimal cost. For many, it feels empowering, no more waiting on developers, no more hefty upfront budgets, just results.

Yet, beneath the excitement lies a sobering reality. What works as a demonstration often fails in production. The same companies that rushed to rely on vibe coding are now hiring professional engineers to repair critical flaws, poor performance, security gaps, systems that cannot scale, and backend complexity that AI could not manage.

This tension between speed and sustainability is not merely a technical challenge, it is also a legal one. Intellectual property ownership, enforceability of contracts, and protection of trade secrets all come into play. Businesses that overlook these dimensions may find themselves not only with unstable software, but also with unclear rights and increased legal risk.

Copyright and Authorship Challenges

In most legal systems, computer programs are protected under copyright law as a category of “literary works.” But applying these rules to AI-generated code is far from straightforward.

1. Who is the author?

Copyright law generally recognises human beings (and sometimes entities through employment or commissioning rules) as authors. AI itself cannot own rights. That leaves courts and regulators to consider whether the person prompting the AI, or the developer refining the output, qualifies as the author.

2. The test of originality

Copyright requires originality, which usually means the application of skill, labour, or judgment by a human. If AI generates code autonomously, the human input may not be enough to meet this threshold. This uncertainty can affect whether AI-generated code is protectable at all.

3. Collaborations and joint works

When freelancers, contractors, or employees fix, extend, or re-architect AI-generated prototypes, their contributions may create “joint authorship.” Without written agreements, ownership can become fragmented, making enforcement difficult.

Companies cannot assume that all vibe-coded outputs are automatically theirs or automatically protected. Human involvement and contractual clarity are essential.

Building a Legal Safety Net

Even the best technical audit cannot solve problems of ownership or misuse unless contracts are carefully drafted. At a minimum, businesses should consider:

1. IP Assignment and Licensing

•  Contractors or employees brought in to fix AI-generated prototypes should sign agreements assigning all intellectual property rights in their contributions.
•  Agreements should specify whether AI outputs form part of deliverables, and how ownership is allocated.

2. Service Level Agreements (SLAs)

• When hiring professionals to repair or scale vibe-coded projects, define scope: are they patching bugs, refactoring architecture, or rewriting from scratch?
• Include realistic limitations on liability. Few professionals will accept responsibility for structural flaws in code they did not originally author.

3. Non-Disclosure Agreements (NDAs)

• NDAs should be in place before sharing prototypes, early-stage code, or even product concepts with external parties.
• Clauses should prevent both disclosure and unauthorised use, ensuring a developer cannot repurpose your idea elsewhere.

4. Confidentiality and Trade Secrets

• Proprietary elements, whether architecture, algorithms, or product features, should be treated as trade secrets.
• Access controls should be implemented to limit who can view repositories, use version control, and log who accessed what, when.

These contractual safeguards transform a fragile experiment into a foundation for enforceable rights and clear obligations.

When to Use AI and When to Call the Professionals

AI tools are accelerators, not substitutes for experienced engineers. The key is to know when to lean on AI and when to invest in professional oversight.

1. Prototype Stage (AI is useful)

• Vibe coding is ideal for brainstorming, quick demos, or proof-of-concept testing.

In light of the nature of the process it is important to keep detailed records of prompts and outputs to show the role of human input in case authorship is later disputed.

2. Pre-Scaling Stage (professional input becomes essential)

• Before onboarding users, investors, or clients, have a qualified engineer review the AI code.

To ensure that legal certainty going forward, commission an audit of security, scalability, and compliance and further update contracts so that any fixes or rewrites are properly assigned.

3. Scaling and Commercialisation

• Move away from fragile AI baselines for mission-critical systems.

At the scaling and commercialisation stage, involving a professional in designing architecture, ensuring compliance with privacy and data regulations, and planning long-term maintenance is a must. In addition, intellectual property protections must be formalised, such as registering trade marks for branding and maintaining trade secret policies.

This phased approach allows founders to capture the creative benefits of AI while mitigating the legal and operational risks.

Practical Checklist for Businesses and Founders

For those entrepreneurs currently utilising vibe coding applications and features to produce an MVP this checklist is useful in ensuring sustainability.

✔ Secure IP ownership through written assignments and licensing terms.

✔ Use NDAs whenever sharing prototypes, concepts, or code externally.

✔ Treat proprietary elements as trade secrets and restrict access.

✔ Define clear SLAs when bringing in professionals to stabilise AI-generated code.

✔ Do not assume AI outputs are automatically protected, document human contributions and originality.

✔ Use AI for speed, but rely on engineers for scale, compliance, and resilience.

Imagine a startup founder who uses AI to create an early prototype of a scheduling app. The app works well enough to impress investors, but when user traffic grows, the system crashes under load. A contractor is hired to rebuild the backend, only to discover security flaws and copied snippets that resemble open-source projects without proper licensing.

If the founder had implemented:

• NDAs before showing the prototype,
• IP assignments from the contractor, and
• clear records of AI prompts and outputs,

they would not only have stronger software but also clear legal ownership. Instead, a lack of foresight risks disputes, investor hesitation, and potential liability for copyright infringement.

Vibe coding represents a promising step for entrepreneurs, allowing ideas to come alive quickly, but it also tempts founders to mistake a prototype for a finished product. Without foresight and professional oversight, the risks extend beyond technical bugs; they include unclear copyright ownership, fractured contracts, and lost trade secrets.

The responsible path forward is not to abandon AI, but to integrate it wisely by prototyping with speed, protecting with contracts, and documenting originality, while scaling with professional engineers.

Disclaimer – The views expressed in this article are the personal views of the author and are purely informative in nature.