As artificial intelligence (AI) tools like ChatGPT, Co-Pilot, Grok and predictive analytics platforms become embedded in everyday business operations, many companies are unknowingly walking a legal tightrope.
While the potential of AI tools provide many benefits – streamlining workflows, enhancing decision-making, and unlocking new efficiencies – the legal implications are vast, complex, and often misunderstood.
From data scraping to automated decision-making, the deployment of AI systems raises serious questions around copyright, data protection, and regulatory compliance.
Without robust internal frameworks and a clear understanding of the legal landscape, businesses risk breaching key laws and exposing themselves to reputational and financial harm.
Senior Partner, Taylor Rose.
GDPR and the Use of AI on Employee Data
One of the most pressing concerns is how AI is being used internally, particularly when it comes to processing employee data. Many organizations are turning to AI to support HR functions, monitor productivity, or even assess performance. However, these applications may be in direct conflict with the UK General Data Protection Regulation (GDPR).
GDPR principles such as fairness, transparency, and purpose limitation are often overlooked in the rush to adopt new technologies. For example, if an AI system is used for employee monitoring without their informed consent, or if the data collected is repurposed beyond its original intent, the business could be in breach of data protection law.
Moreover, automated decision-making that significantly affects individuals, such as hiring or disciplinary actions, requires specific safeguards under GDPR, including the right to human intervention.
The Legal Grey Area of Data Scraping
Another legal minefield is the use of scraped data to train AI models. While publicly available data may seem fair game, the reality is far more nuanced. Many websites explicitly prohibit scraping in their terms of service, and using such data without permission can lead to claims of breach of contract or even copyright infringement.
This issue is particularly relevant for businesses developing or fine-tuning their own AI models. If training data includes copyrighted material or personal information obtained without consent, the resulting model could be tainted from a legal standpoint. Even if the data was scraped by a third-party vendor, the business using the model could still be held liable.
Copyright Risks in Generative AI
Generative AI tools, such as large language models and image generators, present another set of challenges. Employees may use these tools to draft reports, create marketing content, or process third-party materials. However, if the input or output involves copyrighted content, and there are no proper permissions or frameworks in place, the business could be at risk of infringement.
For instance, using generative AI to summarize or repurpose a copyrighted article without a license could violate copyright law. Similarly, sharing AI-generated content that closely resembles protected work may also raise legal red flags. Businesses must ensure their employees understand these limitations and are trained to use AI tools within the bounds of copyright law.
The Danger of AI “Hallucinations”
One of the lesser-known but increasingly problematic risks of AI is the phenomenon of “hallucinations”- where AI systems generate outputs that are factually incorrect or misleading, but presented with confidence. In a business context, this can have serious consequences.
Consider a scenario where an AI tool is used to draft a public document or legal summary, in which it includes fabricated company information or incorrect regulations. If that content is published or relied upon, the business could face reputational damage, client dissatisfaction, or even legal liability. The risk is compounded when employees assume the AI’s output is accurate without proper verification.
The Need for Internal AI Governance
To mitigate these risks, businesses must act promptly to implement robust internal governance frameworks. This includes clear policies on how AI tools can be used, mandatory training for employees, and regular audits of AI-generated content.
Data Protection Impact Assessments (DPIAs) should be conducted whenever AI is used to process personal data, and ethical design principles should be embedded into any AI development process.
It’s also critical to establish boundaries around the use of proprietary or sensitive information. Employees interacting with large language models must be made aware that anything they input could potentially be stored or used to train future models. Without proper safeguards, there’s a real risk of inadvertently disclosing trade secrets or confidential data.
Regulatory Focus in 2025
Regulators are increasingly turning their attention to AI. In the UK, the Information Commissioner’s Office (ICO) has made it clear that AI systems must comply with existing data protection laws, and it is actively investigating cases where this may not be happening. The ICO is particularly focused on transparency, accountability, and the rights of individuals affected by automated decision-making.
Looking ahead, we can expect more guidance and enforcement around the use of AI in business. The UK is currently consulting on its AI Bill which aims to regulate artificial intelligence by establishing an AI Authority, enforcing ethical standards, ensuring transparency, and promoting safe, fair, and accountable AI development and use that businesses must comply with.
AI is transforming the way we work, but it’s not a free pass to bypass legal and ethical standards. Businesses must approach AI adoption with caution, clarity, and compliance to safeguard their staff and reputation. By investing in governance, training, and legal oversight, organizations can harness the power of AI while avoiding the pitfalls.
The legal risks are real, but with the right approach, they are also manageable.
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