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Você sabe como LLMs usam sua marca sem permissão? Descubra os impactos reais
BranddiIP Team ·
Do you still believe that consumers discover your brand only through search engines, advertisements or social networks? Increasingly, the first answer comes from Large Language Models, known asLLMs (Large Language Models).
Today, billions of users turn to AI assistants powered by LLMs. 37% of Brazilians have already claimed to have made at least one purchasing decision influenced by these artificial intelligence models, according to research published in Veja.
But there are risks associated with the interference of LLMs in the consumer journey. Want to know what they are? Check out the guide below and ask your questions!
How do LLMs absorb a brand's content?
In Large Language Models (LLMs), developers often use techniques such as web scraping, that is, automated programs that search, collect and save content available on websites across the internet.
The programs scan forums, blog posts, product pages, companies and virtually any content accessible online to build massive training data sets.
And what are the invisible risks?
Organizations that study the legal and ethical impact of AI, such as the OECD (Organization for Economic Co-operation and Development), highlight that collecting data to train LLMs raises intellectual property and unauthorized use concerns.
The report points out that data collection methods that rely on scraping can give rise to conflicts with copyright and other proprietary rights because content is captured without explicit consent from the original owner.
After collecting these huge data sets, LLMs learn statistical and probabilistic patterns about language.
Researchers in the field of AI note that this can lead to hallucinations (factually incorrect or misleading responses) because the it is generalizing from patterns in the data, not verifying the veracity of the sources.
What is the direct impact on the consumer?
AI responses are already changing how brands are perceived, chosen and evaluated. Below, the main impacts of this new intermediary on the consumer experience.
Confusion about the official channel
When receiving a response that seems neutral and safe, the consumer tends to assume that the link, website or seller mentioned is the official one. Without signs of authenticity,the distinction between legitimate channel and third parties is lost.
Mistaken purchase rotations
Assuming that the recommendation is correct, the consumer follows purchasing paths that were not thought of by the brand, including unauthorized resellers, outdated offers or alternatives inappropriate to the real context of the need.
Erosion of trust
If the AI-driven experience fails, the frustration is not attributed to the model, but rather the brand mentioned. Over time, these small, accumulated breaches weaken trust and the perception of authority.
How to minimize risks?
Some companies are taking steps to reduce distortions and decrease brand exposure in AI-generated responses:
Strengthen official digital presence
The first layer is to increase the density and clarity of official assets, and this involves standardizing domains, subdomains, verified profiles, institutional pages and structured data (schema, semantic markup, metadata). LLMs.
Mitigating this risk requires continuous monitoring, rapid takedowns, formal breach notifications, and active cleanup of incorrect mentions. Reducing the persistence of these signals reduces the chance of them being incorporated and replicated by AI models.
Protecting branded terms in search engines, marketplaces and networks
Whenever branded terms are used uncontrollably, wrong associations form. The application of brand bidding policies, enforcement on marketplaces and blocking of irregular advertisements reduces semantic dispersion and limits distortions that end up being absorbed by LLMs.
Did you like the tips? Shielding your digital signals is the only way to ensure that AIs learn from correct sources, and not from scams or piracy.
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