How to Use Generative AI in Marketing Without Infringing UK Copyright?

For any Content Director in the UK, the pressure to integrate Generative AI tools like Midjourney and ChatGPT is immense. The promise of unprecedented efficiency and creativity is tantalising. Yet, this enthusiasm is matched by a growing fear of the complex legal minefield surrounding intellectual property. The landscape is flooded with vague warnings to “be careful” or “check the terms,” but this advice is insufficient for professionals who need to operate with legal certainty. While recent UK data reveals that 90% of UK marketers plan to increase their AI usage, very few have a clear strategy to manage the associated risks.

The core of the problem is that approaching AI as just another tool is a fundamental mistake. Each prompt, each generated image, and each piece of copy exists within a complex chain of rights and ownership that starts with the AI’s training data and ends with your published campaign. Simply hoping for the best is not a strategy; it’s a liability. But what if the key wasn’t to fear AI, but to master its legal framework? What if the solution wasn’t avoidance, but a structured, defensible process?

This article moves beyond the headlines to provide a practical IP governance framework specifically for UK marketers. We will dissect the nuances of UK copyright law, provide actionable checklists for auditing your tools and content, and establish a risk-based workflow. This is your guide to transforming legal uncertainty into a competitive advantage, enabling you to innovate with confidence.

To navigate this complex but crucial topic, this guide is structured to build your understanding step-by-step. Below is a summary of the key areas we will cover, from the foundational principles of UK copyright to the practicalities of registering AI-assisted assets.

Why AI-Generated Content May Not Be Protectable Under UK Law?

A common misconception is that if you prompt an AI, you own the output. In the UK, the situation is far more precarious. The Copyright, Designs and Patents Act 1988 (CDPA) includes a unique provision for “computer-generated works,” where the author is considered to be “the person by whom the arrangements necessary for the creation of the work are undertaken.” While this sounds promising, it has been largely untested in court for modern generative AI and is the subject of intense debate, as evidenced by the fact that the UK government received over 11,500 responses to its recent consultation on AI and copyright.

The critical threshold is “sufficient human authorship.” A simple prompt like “a cat in a hat” is unlikely to grant you copyright. To build a defensible claim of ownership, you must demonstrate significant creative intervention. This moves your role from a passive commissioner to an active co-creator. Your protection lies not in the AI’s output, but in your documented, creative human input throughout the process. Without this, you may be creating assets that your competitors could potentially use without consequence, as you would have no legal standing to stop them.

To establish a robust claim to authorship, your workflow must include evidence of substantial human involvement. Your team should be documenting:

• Extensive prompt engineering and refinement processes that go far beyond simple text input.
• Significant post-production editing, curation, and arrangement of AI-generated outputs.
• Detailed records of human creative decisions made at each stage of the content’s development.
• The inclusion of substantial human-authored elements alongside any AI-generated content.
• The application of human judgment in the selection and final composition from multiple AI iterations.

Ultimately, your ability to protect and monetise AI-assisted content hinges on proving that the final product is a result of your team’s intellectual effort, not merely the machine’s.

How to Audit Your Content Library for Potential IP Infringements?

For a Content Director, the immediate concern is the existing and future content pipeline. Implementing a systematic audit process is not just good practice; it’s an essential defensive measure. You need to assess the risk profile of the tools your team is using, as the potential for infringement is often baked into the tool’s own architecture. An organised approach to this audit is fundamental to building a sustainable IP governance framework.

As the image suggests, this process requires careful organisation. The primary risk factor lies in how an AI model was trained. Models trained on proprietary or licensed stock data (like Adobe Firefly) present a much lower risk than those trained by scraping vast swathes of the public internet, which may include copyrighted material. Using outputs from the latter could expose your company to “downstream liability” for infringement, even if your team acted in good faith.

A risk-based audit involves categorising your AI tools and establishing clear usage policies for each. This matrix provides a simplified framework for assessing risk and defining necessary actions for your marketing team.

AI Tool Risk Assessment Matrix for UK Marketers

AI Tool Category	Training Data Policy	UK Risk Level	Recommended Actions
Adobe Firefly	Trained on licensed/owned content	Low	Document usage; review terms periodically
Midjourney/DALL-E	Web-scraped training data	Medium-High	Avoid style mimicry; extensive modification required
Open-source models	Variable/unclear sources	High	Thorough due diligence; legal review essential

This tiered approach allows you to empower your team with low-risk tools for everyday tasks while reserving high-risk tools for projects where significant human modification and legal oversight can be applied.

Creative Commons vs Royalty-Free: What Is Safe for Commercial Use?

The complexity of AI and copyright is magnified when the training data itself is subject to specific licenses. Many generative AI tools are a “black box,” giving you no visibility into whether their training data included images or text under Creative Commons (CC), royalty-free, or other open-source licenses. This creates a ticking time bomb for commercial projects, as the licensing terms of the input data can unexpectedly attach themselves to your output.

Royalty-free licenses are generally safer for commercial use, as they typically grant broad usage rights after a one-time fee. However, the real danger lies with certain Creative Commons licenses, particularly those with “ShareAlike” (SA) or “NonCommercial” (NC) clauses. If an AI tool used a “ShareAlike” image in its training, there is a legal argument that your commercially-used output, as a derivative work, must also be licensed under the same open “ShareAlike” terms. This could force you to release your proprietary campaign assets for free, a devastating commercial outcome.

Given the lack of transparency from many tool providers, the most prudent approach is to assume a worst-case scenario. Treat any output from a “black box” AI as potentially tainted and ensure it undergoes substantial human modification to create a new, transformative work that is less likely to be considered a direct derivative.

The ‘Style Mimicry’ Risk That Could Lead to a Passing Off Claim

Beyond direct copyright infringement lies a more subtle but equally potent risk under UK law: the tort of “passing off.” This occurs when a business misrepresents its goods or services as being those of another, or associated with another, causing damage to that business’s goodwill. In the context of generative AI, this is the “style mimicry” risk. Prompting an AI to create an image “in the style of” a famous artist, brand, or even a direct competitor is exceptionally dangerous.

While an artist’s “style” itself is not protected by copyright, their distinctive visual identity is tied to their reputation and goodwill. If your AI-generated marketing asset is similar enough to mislead consumers into thinking the original artist or brand endorsed or created it, you could face a costly passing off claim. This legal risk is compounded by significant brand risk, as research shows that 60% of marketers are concerned about AI-driven brand reputation damage. Authenticity and originality remain paramount.

To mitigate this specific UK risk, your creative workflow must have strict controls over the prompting process. Vague instructions to “avoid copying” are not enough. A clear, documented policy is your best defence.

The goal is to use AI as a tool to execute your unique vision, not as a shortcut to replicate the established goodwill of others. Your prompts should describe the desired aesthetic attributes—mood, colour palette, composition—without naming the creator you seek to emulate.

When to Involve Legal Counsel in the Creative Process?

The default answer, “ask a lawyer,” is unhelpful for a busy marketing department. It creates bottlenecks and stifles creativity. A more effective approach, aligned with a modern IP governance framework, is a risk-tiered system. This empowers your team to proceed with confidence on low-stakes projects while ensuring high-value, high-risk assets receive the necessary legal scrutiny. The key is to define clear triggers for when legal review is mandatory.

Collaboration between creative and legal teams should be a structured process, not a last-minute panic. Involving counsel at the conceptual stage of a major campaign can prevent costly rework later. For instance, a quick review of the proposed AI tools and creative brief for a flagship product launch can identify IP risks before a single asset is generated.

This decision matrix helps you determine the appropriate level of legal engagement based on the specific scenario. It replaces ambiguity with a clear, escalating scale of intervention, allowing you to allocate legal resources where they are most needed.

UK Legal Support Decision Matrix

Scenario	Risk Level	Legal Support Needed	Specialist Required
Campaign value exceeds £50,000	High	Full review	IP/IT specialist
Using AI for regulated sectors (FCA/MHRA)	Very High	Pre-launch approval	Regulatory specialist + IP
Training custom model on scraped data	Critical	Immediate consultation	Data protection + IP specialist
Standard AI tool for blog content	Low-Medium	Periodic policy review	General commercial lawyer

By formalising these triggers, you create a predictable and efficient workflow that balances speed of execution with robust legal risk management, turning your legal team into a strategic partner rather than a roadblock.

The Intellectual Property Oversight That Hands Your Advantage to Rivals

One of the most overlooked risks of using generative AI lies in the fine print of the provider’s Terms of Service. In your team’s quest for the perfect prompt sequence to generate unique, high-converting content, they may be creating a valuable new form of intellectual property. However, many AI platforms include clauses that grant them broad rights to use your prompts—and sometimes even your outputs—to train and improve their models. In effect, you are spending your resources to develop a competitive advantage, only to hand it directly to the AI provider, who can then incorporate that learning into their public model for your competitors to use.

Under UK law, a complex and innovative prompt chain could potentially be protected as a trade secret or as confidential information. This protection, however, is rendered meaningless if you have contractually agreed to sign those rights away by accepting the platform’s terms. This is a critical IP governance failure.

To prevent this, you must treat your most valuable prompt sequences as proprietary IP. This requires a proactive strategy to protect them both contractually and operationally.

• Draft employment and freelance agreements with explicit clauses defining ownership of AI-generated IP and prompt sequences.
• Formally classify your most complex and valuable prompt chains as trade secrets under UK law, and control access accordingly.
• Prioritise on-premise or enterprise-level AI solutions that offer data privacy and guarantees that your inputs will not be used for public model training.
• Implement a system for prompt versioning and access control within your organisation to protect your most valuable sequences.
• Document the unique business value and development process of your proprietary prompt strategies.

Protecting your prompts is as important as protecting the final content. Failing to do so is equivalent to funding your competitors’ research and development.

Proprietary Software vs Open Source: What Suits UK Data Privacy Laws?

Beyond copyright, the use of generative AI in marketing triggers significant data protection obligations under UK GDPR. The choice between a closed, proprietary system and an open-source model has profound implications for compliance. Proprietary software from major providers often comes with a Data Processing Addendum (DPA) and clearer policies on data handling, which can simplify your compliance burden. However, you must still conduct due diligence, particularly regarding data sovereignty—where your data is physically stored and processed.

Open-source models offer flexibility but shift the compliance responsibility almost entirely onto you. You must verify the model’s training data for hidden personal information and ensure your implementation aligns with UK GDPR principles like data minimisation and purpose limitation. As the UK Information Commissioner’s Office (ICO) has made clear, the goal is to balance innovation with the fundamental rights of individuals.

The ICO states that its response aims to provide support and ensuring legal clarity for developers of AI, whilst also balancing the rights of content creators and promoting trust

– UK Information Commissioner’s Office, ICO Response to Copyright Consultation

This statement signals that while the ICO supports AI development, it will not tolerate disregard for data protection law. If your team uses customer data (e.g., for personalising marketing copy with AI), your legal basis for processing that data must be watertight. You must be able to execute a user’s “right to erasure” from both your systems and, potentially, the AI model itself.

Your AI tool audit must therefore include a UK GDPR compliance check:

• Verify the physical data processing and storage locations to ensure compliance with UK data sovereignty rules.
• Confirm if any personal data is used to train public models; this requires explicit, opt-in consent under UK GDPR.
• Ensure the “right to erasure” can be fully executed for any personal data submitted to the tool.
• Check for UK-US Data Bridge or other valid data transfer mechanism compliance for any US-based tools.
• Scrutinise Data Processing Addendums (DPAs) for UK-specific provisions and liabilities.
• Document the lawful basis for any processing of personal data within your AI workflows.

Ultimately, your choice of tool must be guided not only by its creative capabilities but also by its ability to fit within your existing UK GDPR compliance framework.

How to Register a Trademark in the UK and EU Post-Brexit?

The final step in leveraging AI for marketing is protecting the assets you create, such as a brand name or logo. Using AI can be a powerful brainstorming tool, but it also creates specific hurdles for trademark registration. Both the UK Intellectual Property Office (UK IPO) and the EU Intellectual Property Office (EUIPO) require a trademark to be “distinctive” and not “descriptive.” AI-generated marks often tend towards the generic, as they are based on existing patterns, and may be rejected on these grounds.

Substantial human modification is therefore often necessary not just for copyright ownership, but also for trademark registrability. You must add unique, creative elements to the AI’s suggestion to elevate it from a generic design to a distinctive brand asset.

The post-Brexit landscape adds another layer of complexity and cost. An EU trademark no longer provides protection in the UK, and vice-versa. You must now file two separate applications—one with the UK IPO for UK rights and one with the EUIPO for protection across the EU member states. This involves separate fees, separate examination processes, and potentially separate legal representation.

To navigate this process successfully, your IP strategy must budget for these dual tracks. The convenience of AI in the creative phase must be balanced against the rigorous, human-driven effort and increased administrative costs required to secure and defend those assets in a divided UK and EU market.