AI is the best tool ever invented for protecting intellectual property, and the biggest threat to it.
That contradiction sits at the heart of today’s debate. On one side, it makes monitoring for infringement, analysing portfolios, and detecting counterfeits faster and smarter than ever. On the other, it raises difficult questions: who owns AI-generated content, can an AI be an inventor, and what happens if training data includes copyrighted work?
Courts, regulators, and businesses are already wrestling with these challenges. From global rulings on patent inventorship to India’s review of its Copyright Act, the landscape is shifting quickly. Yet patterns are emerging, and there are practical steps to take now.
In this blog, you will learn how AI is reshaping copyright, patents, trademarks, and trade secrets, the risks to watch, and how a hybrid human-plus-AI approach can help you stay protected while making the most of the technology.
The Double-Edged Sword of AI in IP
AI is changing the way intellectual property is protected, but not always in straightforward ways. Think of it as a double-edged sword: it sharpens your ability to defend rights, but it also makes the rules around ownership and protection more complicated.
On the plus side, AI speeds up IP workflows. Tasks that once took hours, like searching for prior art, scanning trademarks, or monitoring large portfolios, can now be automated. Rights holders use machine learning to spot counterfeit listings, track brand abuse, and even predict infringement risks before they escalate.
But there is a catch. Who owns an image, a piece of code, or a research draft created by an AI model? Can an AI be named as the “inventor” on a patent? What liability exists if an AI output infringes someone else’s rights?
Most jurisdictions are cautious. They require a human author or inventor, and they scrutinise AI-generated works more strictly. The result is a tension: AI can help enforce rights better, but it also forces a rethink of how to create, document, and claim ownership of new work.
Copyright Challenges
When it comes to copyright, AI is putting pressure on two big areas: training data and AI outputs.
Training data
Most AI systems are trained on massive datasets that include copyrighted works such as books, music, code, and images. The big question is whether this is fair use or fair dealing, or infringement.Policymakers are split. Some propose transparency about training datasets; others want licensing schemes or levies to compensate creators.
In India, the Commerce Ministry is considering updates to deal with training, ownership, and disclosure, with ideas ranging from new chapters to statutory licensing for training purposes.
AI outputs
Can you copyright what an AI produces? Only works with meaningful human authorship qualify. In practice, this means proving human input through selection, arrangement, or revision to claim protection.
India is also exploring this issue. Discussions around applying fair dealing to training and focusing on whether AI outputs cause market harm suggest a more context-based, balanced approach.
Bottom line
- Keep records of human contribution.
- Prepare for more transparency around datasets.
Patents and Human Inventorship
Patents are another area where AI creates friction. Across most jurisdictions, including the U.S., U.K., Japan, and India, only a human can be an inventor. Courts and patent offices have repeatedly rejected applications that list AI systems such as DABUS as inventors.
If AI helps with research or product development, it is not enough to say “the machine invented this.” To maintain patentability and ownership, document human contribution at every stage, from defining the problem to guiding the system and making inventive decisions based on AI-generated insights.
At the same time, firms use AI to strengthen patent strategies. AI tools can:
- Screen newly filed patents
- Map prior art more efficiently
- Inform freedom-to-operate analyses
This makes the process faster and broader in scope, but not foolproof. AI can surface patterns, but human experts still need to correct errors and assess what really matters.
In short, AI can boost patent strategy, but the legal system recognises the human inventive steps behind it.
Evon Technologies is negotiating the AI and IP landscape with ease and intelligence. Get in touch to know how.
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Trademarks and Counterfeits
AI is giving brand owners new firepower against counterfeits and misuse. Systems can scan marketplaces, social media, and domain registrations at scale to spot:
- Fake product listings
- Typosquats (misspelled domains)
- Misused logos
- Other forms of brand abuse
This speeds detection, shortens takedown cycles, and improves enforcement, with machine-assisted platforms centralising evidence collection against repeat infringers.
But automated flagging can misclassify parody, comparative advertising, or legitimate fair use as infringement. Relying only on automation risks overreach and chilling lawful expression.
The balance is clear: let AI handle detection at scale, but keep legal experts in the loop to validate edge cases.
Trade Secrets and Data Governance
Trade secrets live and die by confidentiality, and AI changes both sides of that equation.
How AI helps
- Detects suspicious access patterns
- Scores insider risk
- Monitors endpoints and cloud systems for data exfiltration
These capabilities improve incident response and make it harder for bad actors to quietly steal information.
How AI hurts
The same technology can increase leakage risks. If sensitive information is fed into a model without the right guardrails, it could be exposed, reused, or incorporated into outputs, potentially destroying its status as a “secret.”
That is why strong governance is critical. Set clear rules for training data classification, segregation of confidential information, and what inputs and outputs are allowed. Without these safeguards, “secret” information might not legally qualify as secret anymore.
In short, AI can be a powerful shield, but only if controls prevent it from becoming an accidental hole in the defences.
What does the regulatory landscape look like?
The rules around AI and IP are moving fast, and they do not look the same everywhere.
In the EU, new frameworks are connecting generative AI to transparency obligations, applied in phases: core prohibitions and literacy measures start in 2025, duties for providers of general‑purpose models begin in 2025, and broader transparency requirements, including machine‑readable labelling of AI outputs and deepfakes, phase in with some obligations from 2026. Expect clearer disclosure of training data and provenance tools such as watermarking.
Internationally, organisations like WIPO are working to align norms so that creators are fairly compensated while innovation is not stifled. National authorities are weighing in. The U.S. is clarifying that human authorship is required for copyright, and jurisdictions are issuing guidance on how much human involvement is necessary for protection. Sector-specific rules for media, code, and datasets are likely on the horizon.
In India, proposals include defining authorship for AI-assisted works, requiring dataset disclosures, and exploring statutory licensing for training. For patents, authorities remain aligned with the global stance: AI cannot be an inventor, so applicants must highlight the human element of innovation in filings.
Practical Protections, Now!
So, what can be done today to protect intellectual property while using AI? The best approach is a hybrid model: let AI handle speed and scale, but keep humans in the loop for judgment and compliance.
Action checklist
- Embed human authorship: Ensure real human input, such as selection, arrangement, and revision, and keep logs documenting who did what.
- Track training sources: Maintain transparency about datasets, licences, opt-outs, and permissions to be ready for disclosure.
- Tighten model governance: Classify data, segregate confidential information, and control inputs and outputs to prevent leaks that compromise trade secrets.
- Fortify enforcement: Use AI to monitor for infringements and counterfeits, but apply human review to edge cases before acting.
- Patent with people in the loop: Record human inventive steps behind AI-assisted research. Keep lab notes showing that patents tie to human creativity, not just machine output.
Evon’s Human+AI Approach
AI is rewriting the rules of intellectual property, but not replacing them. It supercharges detection, monitoring, and portfolio management, while complicating authorship, ownership, and liability.
Across copyright, patents, trademarks, and trade secrets, the law insists on one thing: the human element still matters. Document the contribution, govern the data, and keep experts involved. The smartest strategy now is hybrid.
Evon Technologies is smartly navigating this revolution, with our value-based AI development services. We let AI do the heavy lifting, but put human expertise in charge of the final word, for stronger, more reliable IP protection. On offer are the most versatile and cost-effective solutions for salesbots, customer support bots and AI work assistants. Get a customized plan for your business now.
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