Copyright Risks in Multimodal Generative AI: Images, Music, and Video Clips

You just spent twenty minutes tweaking prompts to get the perfect background track for your YouTube video using Suno is an AI music generation platform that creates songs from text prompts.. You love it. It fits your brand perfectly. But here is the cold reality check: you do not own that song. In fact, if a competitor steals it tomorrow, you have zero legal recourse to stop them. Worse yet, you might be sitting on a lawsuit waiting to happen because that "original" track sounds suspiciously like a hit from 1995.

This is the messy, high-stakes world of Multimodal Generative AI is AI systems capable of processing and generating multiple types of data including images, audio, and video simultaneously. as we move through 2026. The technology has outpaced the law, leaving creators, marketers, and businesses navigating a minefield where every click could trigger a copyright violation or result in unprotected work. Whether you are generating images with Midjourney is a popular AI image generation service known for artistic quality., composing jingles with Udio is an AI music generator competing with Suno in the creative space., or spinning up video clips, the rules of ownership and infringement are shifting under your feet.

The Ownership Trap: Why Your AI Art Is Public Domain

Let’s start with the biggest shocker for most users: in the United States, you generally cannot copyright purely AI-generated content. This isn’t a rumor; it is established policy from the U.S. Copyright Office, which released definitive guidance in January 2025. Their stance is clear: if a human did not determine the expressive elements, there is no author, and therefore no copyright.

This ruling was cemented by the case of Kristina Kashtanova, who tried to register her comic book, "Zarya of the Dawn," created using Midjourney. The Copyright Office rejected the registration for the AI-generated images, arguing that because Midjourney operates unpredictably, the user cannot control the output enough to be considered an "author." Without that registration, you face profound difficulties enforcing any rights you think you have. If someone copies your AI-generated logo, illustration, or character design, you cannot sue them for copyright infringement because you never owned the copyright in the first place.

This creates a dangerous business paradox. You invest time, money, and creative direction into generating assets, but those assets fall into the public domain instantly. Competitors can use your exact AI-generated imagery without compensation or consequences. For brands relying on unique visual identity, this means your competitive advantage evaporates the moment you publish the file.

The Music Paradox: No Protection, High Liability

The situation gets even more precarious when we talk about music. Platforms like Suno and Udio explicitly state in their terms of service that they make no warranty that any copyright will vest in the output. This is a massive red flag for commercial users. If you generate a jingle for your podcast, anyone else can legally use that same jingle. You have invested resources, yet you hold no exclusive rights.

But the risk goes both ways. These AI models are trained on massive datasets of copyrighted songs. Because machine learning works by finding patterns in existing data, the outputs almost certainly resemble something in that training set. Legal analysts predict that courts will likely find that AI-generated music constitutes copyright infringement if it resembles protected works. This leaves you in an untenable position: your creation is uncopyrightable (so you can’t protect it), but it might infringe on someone else’s copyright (so you can be sued).

Consider the difference between composition and sound recording. Under English copyright law, specifically Section 178 of the Copyright, Designs and Patents Act 1988, computer-generated works can theoretically receive protection, but the distinction matters. Sound recordings often receive protection regardless of originality, while lyrics and compositions must be "original." In the U.S., however, the blanket rejection of non-human authorship means neither the melody nor the recording is safe from being copied by others, while still risking litigation from rights holders whose work trained the model.

Image Generation: Stealing Style, Siphoning Income

For visual artists, the threat is not just about ownership; it is about economic harm. Litigation against Stable Diffusion creators highlights a specific injury: style mimicry. Until recently, if a client wanted an image in the style of a specific artist, they had to commission that artist. Now, clients can input the artist’s name into an AI tool and generate unlimited variations without paying the creator.

This represents a direct siphoning of commissions. Artists are seeing their distinct styles-developed over decades of practice-replicated by algorithms trained on their unauthorized work. The complaint documents cite that purchasers can now use an artist’s works contained in the training data along with the artist’s name to generate new works without compensating the artist at all. This is not just theoretical; these AI-generated images are already being sold online, undercutting human professionals.

If you are a business using these tools, you inherit this liability. Using a prompt like "in the style of [Living Artist]" may not only be ethically questionable but could expose you to claims of unfair competition or trademark dilution, depending on how the likeness is used. The line between inspiration and infringement is blurred, but the financial impact on human creators is sharp and immediate.

Video Clips: The Multi-Layered Liability Nightmare

Video generation combines the worst parts of image and audio copyright issues. A single AI-generated video clip involves cinematography, underlying musical composition, sound recordings, and potentially performer rights. Each of these layers carries its own copyright status. If any component of the generated video incorporates or resembles copyrighted material from the training data, your liability exposure multiplies across multiple categories.

Imagine generating a promotional video where the AI synthesizes a voice that sounds exactly like a famous actor, uses a background track reminiscent of a pop hit, and features visual aesthetics copied from a blockbuster movie franchise. You are not just risking one lawsuit; you are opening yourself up to claims from producers, record labels, and talent agencies simultaneously. The legal framework for video copyright is less established than for static media, meaning courts have even more discretion to rule against defendants in novel cases.

The Trust Crisis: Bad-Faith Registrations

A critical emerging concern is the integrity of the copyright database itself. Legal experts warn that creators will simply lie about the role of AI when applying for copyright. They will claim human authorship for works that were largely or entirely generated by machines. Before long, we could see thousands of fraudulent registrations cluttering the system.

This corruption of the registry creates cascading effects. Legitimate creators cannot verify whether claimed copyrights are valid. Litigation becomes harder to resolve because registration evidence is no longer trustworthy. The entire system of copyright protection weakens as the database loses its value as a source of truth. For businesses, this means due diligence becomes exponentially more expensive and complex. You can no longer trust that a registered copyright actually belongs to the person claiming it.

Voice and Likeness: The Personal Rights Frontier

Beyond traditional copyright, there is the issue of personality rights. Artists and public figures are increasingly concerned about the unauthorized use of their voice and likeness in AI-generated works. Contracts need careful review to identify clauses that might grant away full publicity or simulation rights. Boilerplate talent agreements often bury provisions that allow studios or labels to use digital replicas of performers without additional consent.

Legal guidance emphasizes that rights holders must negotiate explicit agreements for the use of likeness. Licensing agreements or releases should clearly explain the purpose of the works and how intellectual property will be used. If you are creating AI content that mimics a specific person’s voice or face, you need written permission. Relying on "fair use" defenses for deepfakes or voice clones is risky and increasingly unpopular in courtrooms.

Navigating the Uncertainty: Practical Steps for 2026

So, what do you do? Do you abandon multimodal AI? Probably not, given its efficiency and creativity. But you must change how you use it. First, assume that any purely AI-generated output is free for anyone to use. Do not rely on it for proprietary branding unless you add significant human creative input that transforms the work into something distinctly human-authored.

Second, document everything. Keep records of your prompts, iterations, and especially any human modifications made in post-production. If you are trying to argue for copyrightability based on human authorship, your documentation is your only defense. Misrepresenting AI content as human-created in copyright applications is fraud, so stay honest but strategic.

Third, look toward licensed solutions. As of 2026, licensing mechanisms are beginning to emerge. Deals like Hipgnosis’s partnership with Reactional Music show that rights holders are willing to license their catalogs for AI training if compensated properly. Using platforms that train on licensed data reduces your infringement risk significantly. While more expensive, this "clean" training data offers peace of mind that open-weight models trained on scraped internet data cannot provide.

Finally, consult legal counsel before commercializing high-value AI assets. The landscape is built on sand, and the ground is shifting. What is permissible today might be illegal tomorrow following a key appellate decision. Protect your business by understanding that speed and cost savings come with hidden legal liabilities that could far outweigh the initial benefits.