Generative AI is transforming industries by creating text, images, music, and more. However, what challenge does generative AI face with respect to data?
The effectiveness of generative AI relies on vast amounts of data, but issues such as data quality, availability, privacy, and bias are significant issues. In this guide, we explore the data challenges, look at how generative ai has affected security and discuss potential solutions for ensuring responsible implementation.
What challenges does generative AI face with respect to data?
As generative AI evolves, managing and safeguarding data presents significant challenges. Here are some of the main risks.
Data quality
To train AI models large-scale datasets are needed. These extensive datasets enable AI to learn intricate patterns and generalise effectively, leading to more precise and valuable predictions.
However, acquiring high-quality, diverse, and representative data is a challenge. Many AI models rely on datasets that may be incomplete, outdated, or contain errors. For instance, ChatGPT can produce fabricated data that appears authentic – a phenomenon known as hallucination.
Data privacy and security
The integration of AI across most sectors has heightened concerns about data privacy and security. AI systems often require vast amounts of personal data for training, leading to legal and ethical dilemmas surrounding consent and data usage.
The General Data Protection Regulation (GDPR) enforces strict guidelines on data collection, processing, and storage to protect individual privacy. However, despite these regulations, AI systems remain vulnerable to data breaches and security lapses.
For instance, the AI-driven data analytics firm DeepSeek experienced a significant security breach, exposing over one million sensitive records, including chat logs and internal operational data.
Also, AI-generated content can inadvertently reveal confidential data. For example, large language models (LLMs) have been found to expose sensitive corporate and personal information, introduce unsafe code or data, and be manipulated to divulge illicit information.
Bias in training data
Bias in AI training data is a major challenge because AI systems learn from the information they are given. If that data is skewed or unbalanced, the AI can amplify existing biases, leading to unfair or discriminatory decisions.
For example, if an AI system is trained mostly on data from one group of people, it may struggle to accurately understand or respond to people from other backgrounds.
Many biases in AI come from historical and systemic inequalities. AI models are trained using past data, and if that data contains discrimination, the AI may repeat and even reinforce those unfair patterns.
Studies have found that some AI hiring systems prefer male candidates over female candidates or favour white-sounding names when screening job applications. A study from the University of Washington found that AI tools preferred white-male-associated names 85% of the time, showing how these biases can lead to unfair hiring decisions.
Copyright and intellectual property
Generative AI models often rely on vast amounts of copyrighted materials (such as books, art, music, and news articles) to learn patterns and produce new content. This practice has sparked legal challenges from artists and content creators whose works are being used without permission or compensation.
For instance, French publishers and authors sued Meta, alleging unauthorised use of their copyrighted works to train AI systems. Another example involves Stability AI, Midjourney, and DeviantArt, which faced a lawsuit alleging that their AI models were trained on copyrighted artworks without artists’ consent.
Debate also centres on whether AI-generated works should receive copyright protection. In the UK, copyright law allows protection for works created by computers, even when no human directly creates them. Under Section 178 of the Copyright, Designs and Patents Act (CDPA) 1988, if a computer generates a work, the copyright belongs to the person who made the necessary arrangements for its creation (this could be a programmer, company, or AI user). However, these AI-generated works only receive 50 years of copyright protection, compared to 70 years for traditional works, and they do not have moral rights (such as the right to be credited).
There is some confusion over the difference between AI-generated and AI-assisted content. If a human heavily influences the result, does that make the work human-authored or still AI-generated? This is important because it affects who holds copyright and who is responsible for any legal issues. Some companies want users to be responsible for AI-generated content, while others have promised that users will not be liable for copyright infringements from using their AI tools.
AI manipulation and deepfakes
The rise of AI-generated deepfakes and misinformation is a major concern, with the potential to distort reality, manipulate public opinion, and threaten global security.
Advanced AI models can create highly realistic fake news, altered videos, and fabricated audio, making it increasingly difficult to distinguish fact from fiction. These AI-generated threats are not just theoretical — they are already influencing politics, cybersecurity, and financial fraud.
Deepfake technology can be used to discredit politicians, spread disinformation, and undermine democratic processes. AI-generated videos of public figures making false statements can mislead voters, incited unrest, and manipulate financial markets.
The growing involvement of influential figures like Elon Musk in AI development raises concerns over who controls these technologies and how they are regulated. With AI companies gaining unprecedented power, there is a real risk of AI being weaponised for political or corporate influence.
Deepfake scams are also a major cybersecurity threat. Criminals use AI to create fake audio and video that can impersonate CEOs, celebrities, or government officials to gain access to financial accounts, sensitive data, or confidential conversations. Some AI-generated scams have already cost businesses millions of dollars.
What steps can your organisation take to mitigate the risks?
Organisations that proactively manage AI risks reduce legal and reputational threats and enhance trust. Here are some steps to follow to ensure generative AI is used safely and effectively.
1. Use high-quality, legally sourced data
Ensure AI models are trained on licensed, diverse, and legally obtained datasets. Avoid scraping web content without permission and instead rely on public domain, creative commons, or proprietary data.
The UK Government’s Open Data Portal provides legally safe datasets for AI training. Businesses should also keep detailed records of data sources and permissions to demonstrate compliance.
2. Regularly audit and clean data
Over time, datasets become inaccurate, outdated, or biased, affecting AI performance. Conduct routine audits using automated validation tools like Talend, which help clean, structure, and validate data.
Human oversight should supplement these tools to ensure any flagged errors, missing data, or duplicates are corrected. Maintaining a central record of data sources also helps improve traceability.
3. Review AI outputs before deployment
Before deploying AI-generated content, businesses should review and verify its accuracy and reliability. In high-risk areas like finance, legal, and healthcare, ensure human oversight, fact-checking, and accuracy reviews are part of the workflow.
Platforms such as Full Fact’s AI-powered fact-checking tool can help verify information before publication. Establishing feedback loops ensures continuous improvements based on real-world performance.
4. Minimise data collection and improve security
Only collect essential data to reduce privacy risks. AI models should process anonymised or pseudonymised data where possible.
Tools like Northdoor help businesses mask or de-identify sensitive data while maintaining its usability. Data should be stored securely with encryption and access controls, and businesses should conduct penetration testing using services like NCSC’s Cyber Essentials to identify vulnerabilities.
5. Train employees on data privacy
Employees should understand how AI handles personal data and the risks involved, including data breaches, unauthorised access, and misuse of personal information.
Regular training should cover GDPR compliance, best practices for handling sensitive data, and recognising potential risks in AI-driven processes. Consider offering UK GDPR Training to provide staff with up-to-date knowledge and practical guidance on safeguarding data in an AI-driven workplace.
6. Detect and prevent AI-generated misinformation
Deepfake scams and AI-generated misinformation pose security and reputational risks. Use AI-powered detection tools such as Sensity, to identify manipulated media and AI-generated fraud.
Organisations can also integrate automated content verification into their workflows, particularly for news, marketing, and customer interactions. Automated content verification verifies the authenticity and accuracy of content.
Training employees to recognise deepfake scams and phishing attempts through Cyber Security Training and Generative AI Training adds an extra layer of defence.
6. Reduce bias in AI decision-making
AI models can reinforce discrimination and unfairness if trained on skewed datasets. Use diverse and representative data sources, testing models across multiple demographic groups to ensure fairness.
The Alan Turing Institute’s Understanding artificial intelligence ethics and safety offers UK-specific frameworks for bias detection and mitigation. Conducting bias audits using tools like IBM AI Fairness 360 can help uncover discriminatory patterns in AI-generated decisions.
7. Establish clear AI use policies
Defining how AI should be used in an organisation prevents misuse and confusion. The Department for Science, Innovation and Technology provides guidance on responsible AI practices.
Internal policies should explain acceptable AI-generated content, compliance requirements, and ethical guidelines. Training employees on these policies ensures they understand AI’s limitations and how to deploy it responsibly.
8. Label AI-generated content clearly
Transparency in AI-generated content is essential to maintaining trust. Businesses should clearly label AI-written reports, customer service chatbots, and marketing materials.
UK regulators, such as Ofcom, have highlighted the need for AI transparency in media and advertising.
Implementing watermarking techniques or disclaimers ensures that customers and stakeholders can distinguish between AI-generated and human-created content.
9. Stay up to date with AI regulations and legal risks
AI laws and copyright rules are rapidly evolving. The Intellectual Property Office (IPO) regularly updates guidelines on AI-generated content and copyright ownership.
It is important to monitor changes in GDPR, AI governance policies, and copyright laws to ensure compliance.
Praxis42 Generative AI for Business Course
What challenge does generative AI face with respect to data? From ethical risks to compliance concerns, understanding how to use AI responsibly is crucial for organisations.
Our Generative AI for Business course provides the skills to drive creativity, innovation, and efficiency while prioritising data security, legal compliance, and ethical considerations. Learn how to integrate AI into your workflows, craft precise prompts, and tailor AI to your organisation’s needs.
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Adam Clarke
Managing Director (Consulting)
