AI and IP for businesses: Protecting your content and your brand

The fastest route to an intellectual property headache with artificial intelligence is also the simplest: someone copies and pastes the wrong thing.

A sales lead, a draft contract, a customer complaint with personal details, a chunk of source code, a pricing table. It goes into a generative AI tool because it is quicker than writing from scratch. The output looks polished. The risk is invisible, until it isn’t.

UK regulators have long framed security as a duty to prevent “unauthorised disclosure” through “appropriate technical and organisational measures”, and that logic applies whether data leaks via email, a shared drive, or a prompt box.

Next comes the slow leak: proprietary material uploaded into third-party tools, then retained, reused, or folded into future product improvement. The national policy argument about who can use copyrighted works to train models is still live, but government documents already set out the direction of travel: more control for rightsholders, more transparency about training, and clearer rules.

Then there is brand misuse, now turbocharged by synthetic media. Fraudsters do not need your logo in high resolution if they can clone a tone of voice, a face, or a customer support script. Advertising and consumer regulators are treating AI-made content as fully in-scope, with the Advertising Standards Authority pointing to proactive monitoring and its Scam Ad Alert System run with platforms.

Financial services has been living with industrial-scale impersonation for years: the Financial Conduct Authority says it receives thousands of reports of fake FCA scams, and it publishes warnings about scammers posing as officials to extract information.

Finally, there is the risk in what comes out. AI outputs can collide with copyright, trade marks and confidentiality, sometimes by mimicking distinctive content, sometimes by reproducing watermarks or branded elements. UK copyright policy has explicitly raised questions about “computer-generated works” and who counts as the author, which is another way of saying the ground is not settled.

What to lock down

Data
Start with blunt rules that staff can remember.

• Don’t paste: customer personal data, contracts, credentials, unreleased financials, pricing, source code, product roadmaps.
• Do paste: already-public information, approved boilerplate, and synthetic examples that contain no real identifiers.

Back this up with controls people barely notice: single sign-on to approved tools, blocking unmanaged accounts, and logging that makes “shadow AI” visible.

If you are building or integrating AI into products, treat it like a system, not a feature. The UK government’s Code of Practice for the Cyber Security of AI is plain about the basics: identify and protect assets, secure the supply chain, document prompts and data, and monitor system behaviour.

Brand assets
Assume your brand kit will escape. Prepare as if it already has.

• Maintain a canonical “official channels” page and keep it updated.
• Use verified accounts where platforms offer them.
• Register trade marks where it matters most for takedowns and marketplace enforcement.

For online marketplaces, make it operational: assign owners, keep trademark certificates to hand, and use platform-compliant tools and brand registries. UK government guidance on e-commerce enforcement points businesses to these mechanisms.

Staff behaviour
Make the safe path the easy path.

• Provide pre-approved prompts for routine tasks that do not require real data.
• Create an internal “AI request” route for high-risk needs (drafting external statements, image generation for campaigns, customer communications at scale).
• Train comms teams on synthetic media escalation: how to capture evidence, who to contact, and how to correct the record quickly.

Trade secrets law, in practice, is about habits. UK guidance talks about taking “reasonable steps”, including identifying secrets, training staff and managing cyber risk.

The contracts and policies organisations are tightening

The most effective AI clauses are not grand statements about “compliance”. They are boring specifics.

Businesses are increasingly insisting that suppliers spell out whether customer content is used for training, how long prompts are kept, which subcontractors touch data, and what happens on deletion requests. The same goes for breach notification and support access. If a supplier cannot explain retention and reuse in a sentence, that is a risk signal.

Internally, the policies that work are short. One page, written like a fire drill, not a lecture. What tools are allowed, what data is banned, and what needs sign-off.

Monitoring that treats misuse as routine

Brand abuse is not a one-off crisis anymore. It is a queue.

The monitoring plan looks like any other operational discipline: alerts for lookalike domains, scans for fake accounts, checks on app stores, and a takedown process that does not depend on a single hero. Track time-to-removal. Track repeat offenders. Build relationships with platform contacts where possible.

The point is not perfection. It is speed, consistency, and reducing the number of easy wins for scammers and copycats.

AI did not invent these risks. It just makes them cheaper, faster, and more scalable. The response is the same: lock down what matters, write it down, and keep watch.