{"id":21267,"date":"2026-08-26T13:48:21","date_gmt":"2026-08-26T13:48:21","guid":{"rendered":"https:\/\/letslaw.es\/?p=21267"},"modified":"2026-08-26T13:48:21","modified_gmt":"2026-08-26T13:48:21","slug":"the-use-of-generative-ai-in-companies-main-legal-challenges","status":"publish","type":"post","link":"https:\/\/letslaw.es\/en\/the-use-of-generative-ai-in-companies-main-legal-challenges\/","title":{"rendered":"The Use of Generative AI in Companies: Main Legal Challenges"},"content":{"rendered":"

The adoption of generative AI tools in companies can no longer be analysed solely from a technological or efficiency-driven perspective. Their use to draft documents, summarise meetings, generate code, prepare commercial proposals or automate customer interactions raises significant legal implications affecting different areas of regulatory compliance. In the European context, moreover, this debate is no longer merely theoretical: the EU Artificial Intelligence Regulation<\/a>, data protection rules, intellectual property law, transparency obligations and, in certain cases, labour and consumer law all coexist within the same legal landscape.<\/p>\n

The key question, therefore, is not whether a company may use generative AI, but under what conditions it can do so safely and in a legally controlled manner. The risk lies not only in the content produced by the tool, but in the entire process: what information is entered, where it is hosted, whether it may be reused, what decisions rely on it, and what degree of human oversight is in place.<\/p>\n

Data Protection and the Use of Personal Information<\/h2>\n

One of the first areas of risk arises when a company inputs personal data relating to customers, employees, candidates or suppliers into AI tools. Emails, contracts, claims, CVs or internal documentation may contain information subject to the GDPR, meaning that their processing requires an appropriate legal basis, compliance with the principles of data minimisation and purpose limitation, and the implementation of adequate security measures.<\/p>\n

The issue becomes even more sensitive when AI is used to support decisions producing significant effects on individuals, for example in recruitment, evaluation or human resources management processes. In such cases, it is not enough to rely on the system’s output: genuine human oversight, traceability and prior risk assessment are essential.<\/p>\n

Confidentiality and Trade Secrets<\/h2>\n

Another major challenge concerns the protection of confidential information. In practice, many incidents do not stem from complex technical failures, but from poorly controlled day-to-day use: employees copying and pasting sensitive information into external tools without properly assessing the consequences.<\/p>\n

Uploading business strategy, know-how, source code, contractual documentation or customer data into such systems may compromise confidentiality and even undermine the protection of trade secrets. For this reason, companies should not merely recommend the “prudent use” of AI, but instead establish clear rules on which tools are authorised, what information may not be shared, and what internal controls govern their use.<\/p>\n

Intellectual Property and Rights Over Content<\/h2>\n

Generative AI also raises important questions from an intellectual property perspective. On the one hand, it is necessary to assess what happens to the content used as input: texts, images, databases, code or third-party materials that may be protected. On the other hand, companies must consider the legal regime applicable to the outputs generated by the tool, as well as the real possibilities for using, reusing or exploiting them.<\/p>\n

From a corporate standpoint, this requires careful review of the provider’s terms and conditions, the applicable licences, and the level of assurance offered regarding the use of protected materials. It is not enough for a tool to be technically useful; it must also provide a reasonable degree of legal certainty.<\/p>\n

Transparency and the Relationship with Clients and Users<\/h2>\n

When generative AI is used in customer service, marketing, communications or content creation, transparency requirements also come into play. Companies must consider whether it is necessary to inform users that certain interactions or content have been generated, assisted or modified by AI, particularly where their appearance may be misleading or create a false impression of human involvement.<\/p>\n

This issue is not only regulatory, but also reputational. The opaque use of such tools may erode the trust of clients and users, especially in sectors where reliability, authenticity or confidentiality are essential to the relationship.<\/p>\n

Liability and Internal Governance<\/h2>\n

A common mistake is to assume that all liability rests with the provider of the tool. However, the company deploying or integrating it into its processes retains its own obligations of control, supervision and compliance. It is therefore essential to review the organisation’s contractual position, the intended use of the tool, access to data, the possible reuse of information, and the allocation of responsibilities in the event of an incident.<\/p>\n

The appropriate legal response is therefore to implement an internal governance framework<\/a>. At a minimum, this requires defining an internal AI use policy, reviewing risks relating to data protection and information security, analysing contractual clauses with providers, and training staff in the proper use of these technologies.<\/p>\n

What Should Companies Do?<\/h2>\n

As a general rule, the appropriate legal response is not to impose an absolute ban on the use of generative AI, but to integrate it into a reasonable control framework. From a compliance perspective, there are at least four priority actions.<\/p>\n

First, companies should adopt an internal AI use policy defining authorised tools, permitted uses, excluded categories of data and levels of internal approval.<\/p>\n

Second, they should carry out a data protection and information security review, especially where the tool may process personal data, sensitive information or confidential documentation.<\/p>\n

Third, the contractual framework with providers should be reviewed, both in relation to data processing and with regard to intellectual property, confidentiality, subprocessors, technical support and liability allocation.<\/p>\n

Lastly, it is essential to strengthen staff training and AI literacy, not as an abstract recommendation, but as a practical requirement of compliance and governance.<\/p>\n

Ultimately, generative AI does not give rise to a single legal issue, but to a set of risks spanning data protection, confidentiality, intellectual property, employment relationships, transparency and corporate liability. For that very reason, its implementation within companies should not be managed solely from a technology or business perspective. It requires a cross-cutting, preventive and well-documented approach. The best-prepared company will not be the one that adopts the greatest number of tools, but the one that knows how to do so in accordance with clear principles of governance, supervision and compliance.<\/p>\n

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