AI compliance: How to train your AI...

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Using first-party data to train your AI models could help you develop new products or services that really resonate with customers. 

But there’s a catch. Because with great power comes serious responsibilities—especially when it comes to AI compliance, privacy and security.  

Modern compliance and security is about proactively building a secure, trustworthy foundation designed to keep up with evolving AI regulations like the EU AI Act and data protection laws. If you can nail this balance, you can get on a firmer footing for sustainable growth and success in a competitive market.   

Let’s look at how you can leverage the full potential of AI while keeping AI compliance and security at the forefront. We’ll break down the benefits that can give your business an edge, the risks you need to be aware of, and the steps you can take to make sure your approach to privacy and compliance is as strong and forward-thinking as your AI strategy.

Training AI with first-party data

Training AI with first-party data—the data your organisation collects directly from customers with their consent—can give you a serious edge. Unlike general AI models trained on publicly available data, models trained on your own data can meet your specific needs so you can create more personalised and innovative products or services.  

However, using customer data for AI also comes with challenges ranging from maintaining data privacy to ensuring you stay compliant with regulations like GDPR. To harness the potential of AI, you'll need to manage your data effectively while overcoming various technical and legal obstacles. Let’s explore the steps involved in building AI models with customer data and how you can address the challenges that arise along the way.

RELATED: The EU AI Act: What are the obligations for providers? Discover your legal obligations if you're creating or developing AI systems intended for the EU market.

Preparing your customer data for AI training

Training an AI model requires gathering large amounts of data and careful data curation.You'll need to process, clean and structure your first-party data (such as customer interactions, transaction records, or operational metrics) to enable the AI to understand and learn from it. Poor data quality can lead to biased models or models that fail to generalise, which can undermine business goals. 

Example: A healthcare company might use customer patient data to train a diagnostic model. Inaccurate or incomplete records could result in incorrect predictions, so the data must be rigorously cleaned, labeled, and verified before use.

• Tip for your organisation: Put a strong data governance framework in place to keep your data clean, consistent and reliable. This means setting clear policies on collecting, storing, and using data to meet your business goals while staying compliant with regulations like GDPR and HIPAA.

Building custom AI Models for customer use

Once your customer data is ready, you can train custom AI models that reflect your business challenges and goals. Training AI on first-party data allows you to create solutions that are closely aligned with your customer's needs, whether you're developing a personalised recommendation engine or an AI-powered chatbot.

For instance, if you train a large language model (LLM) on customer support transcripts, you can build a bot that responds to customers based on real-life interactions. This can help your business deliver more relevant, timely and effective support when people need it.

• Tip for your business: Protect the data and the model-building process by ensuring all data is encrypted during storage and transmission. Think about adding access controls to stop unauthorised access to sensitive information.

Balancing innovation with Data Privacy and Compliance

We can't overstate this one. One of the most critical issues when using customer data to train AI models is ensuring compliance with data privacy regulations. You need to make sure your approach respects data privacy regulations like GDPR and CCPA. As an organisation, it's essential to balance innovation with compliance to protect customer data while staying within the boundaries of the law.

To keep privacy top of mind, think about using techniques like federated learning. With this approach, you train your AI models locally, so sensitive data stays where it belongs—on the devices. Instead of sharing raw data, you just share model updates between servers, which cuts down the risk of breaches. 

Example: In a federated learning setup, a company can train an AI model across decentralised data sources (e.g., local servers or individual devices) without aggregating customer data in a central location. Only the model’s updates are shared, not the raw data itself. 

Another important consideration when developing AI systems is whether they operate as closed or open systems.

• Closed systems: Data processing takes place in an isolated environment, and only a limited group of users has access. Control over input and output data lies with the users, and the entered data is not used for further training.

• Open systems: These AI applications are accessible as cloud solutions over the internet and can use data to respond to other users’ requests. This creates the risk that personal data may be further processed or made accessible to unauthorised third parties. Data transfers to third countries are often involved, requiring AI compliance with specific data protection regulations.

From a data protection perspective, closed systems are generally preferable due to their lower risk.

• Tip for your company: Integrate privacy-first technologies like federated learning or differential privacy into your AI development processes, and carefully consider whether to deploy open or closed systems based on your data protection strategy. This will help you comply with regulations and build customer trust by ensuring you’re handling their data securely.

Scaling AI models as your business evolves

As your business expands and your needs shift, the amount and complexity of the data you handle will grow, too. Scaling your AI models to keep up with this means you'll need more computing power and stronger infrastructure to manage everything smoothly. 

Whether you’re running personalised AI-driven services across different regions or building AI models to improve product recommendations, your systems must be capable of managing large datasets efficiently. 

For example, if your company is expanding into new markets, your AI models need to be able to process data from different regions while maintaining accuracy and speed. This is where scalable, cloud-based AI platforms come into play. 

Tip for your organisation: Think about using cloud-based AI platforms to handle the growing data and computing demands. If you prefer more control, make sure your in-house infrastructure is strong enough to support the training and deployment of larger AI models.

AI compliance best practice

Using customer data responsibly to train AI models opens the door to innovation, better customer experiences, and smoother operations. Here are three ways to approach AI innovation while staying compliant and building trust.

1. Gain a competitive edge

When you use personal data effectively, you can create products and services that give you a leg up on the competition. Imagine understanding your customers better than anyone else—AI can make that happen, helping you offer more personalised experiences that set your business apart.

Action Step: Work closely with your data privacy team to ensure your AI initiatives align with data protection requirements right from the start. Carry out Data Protection Impact Assessments (DPIAs) to proactively embed data protection into your AI initiatives. Think of it as a partnership between innovation and AI compliance—both need to be in sync for your business to thrive.

2. Enhance customer experience

AI has the power to transform how you interact with your customers. Personalised recommendations, predictive analytics, and super-efficient customer service can all be enhanced by AI. The result? Happier customers who are more likely to want to do business with you.

Action Step: Work closely with your product and legal teams to build AI systems that can impress customers and respect their privacy. Being transparent and making your AI decisions easy to explain will help strengthen customer trust and loyalty.

3. Boost operational efficiency

Could your operations run more smoothly with a little help from AI? Automating tasks, reducing costs, and improving decision-making processes are just a few ways AI can make your business more efficient. Imagine cutting down on manual tasks and focusing on what really matters.

Action Step: Look for AI tools that streamline your operations and enhance your compliance efforts. When implementing external AI tools, carry out a Vendor Risk Assessment (VRA) to make sure that your new solution meets the necessary compliance level before deploying it. It’s about finding that sweet spot where efficiency dovetails with regulatory requirements.

Risks and challenges when building AI-powered products

While AI offers immense potential for innovation, using personal data to train models also comes with significant risks. From safeguarding your company’s reputation to avoiding legal trouble, it’s crucial to navigate these challenges carefully. Here’s what you need to know to keep your AI initiatives on track while staying compliant, secure, and trusted.

1. Protect your reputation

No one wants to deal with a data breach or a privacy violation, but it’s a risk when using personal data for AI. If something goes wrong, your company’s reputation could take a hit. Think about how a single incident could impact customer trust and your bottom line.

Action step: Take charge of your data governance policies. Regularly review and update them to stay ahead of potential threats. Refine your internal processes to ensure that you are ready to handle multiple data subject requests efficiently and compliantly. And remember to assess your AI models for biases that could harm your reputation.

2. Avoid legal and financial penalties

Fines and legal issues are the last things you want to deal with, right? Non-compliance with data protection regulations like the GDPR can lead to hefty penalties. If your AI projects are up to code, it could save your business a lot of money.

Action Step: Make AI compliance a priority from day one. Set up a strong framework that includes regular audits, legal reviews, and training for your team. By staying on top of these requirements, you’ll keep your AI initiatives on the right side of the law.

3. Maintain customer trust

Trust is hard to earn and easy to lose, especially when it comes to personal data. If your customers feel their data is being mishandled, they could walk away—and take their business with them.

One way to maintain this trust is by setting up technical and organisational measures that support transparency:

• Explainable AI: Use algorithms and models to make understandable decisions and explain how you achieve the results. This can be supported by using techniques such as decision trees or transparent neural networks
• Bias detection tools: Use tools that identify potential biases in the data or models to ensure the AI system operates fairly and without discrimination
• Documentation of data processing: Create a record of processing activities that clearly describes AI-supported data processing steps and their purposes

Action Step: Be transparent about how you use customer data. Make sure your communications are clear and accessible. Consider setting up a privacy portal where customers can easily find information about your data practices and exercise their rights. These technical measures will support transparency and enhance customer confidence.

4. Keep control of costs

Implementing strong data protection measures isn't cheap. But skimping on these measures could cost you more in the long run, especially if you face a data breach or compliance issue.

Action Step: Invest in compliance tools that also improve operational efficiency. It’s all about getting the most bang for your buck—protecting your business while keeping costs under control.

Understanding the EU AI Act: A new era for AI governance

The EU AI Act is a groundbreaking regulation that is set to change the landscape of AI usage across Europe and beyond. Understanding this Act is critical for businesses involved in AI, whether you're developing AI tools or deploying them in your operations. The Act introduces strict guidelines to ensure organisations develop and use AI responsibly, prioritising transparency, fairness, and compliance with fundamental rights.

How the EU AI Act affects you as a provider or deployer

As a provider, the Act requires you to ensure that your AI systems are designed with privacy and AI compliance at their core. This means rigorous testing, documentation, and transparency about how your AI models function and make decisions. You’ll need to regularly monitor these systems to detect and mitigate any risks, ensuring they don’t infringe on users' rights or introduce biases.

For those deploying AI, the responsibilities are just as significant. Those deploying AI have just as many responsibilities. You’re required to perform continuous monitoring, ensure transparency, and take corrective actions if any risks or issues arise. The Act pushes for a holistic approach to AI governance, integrating AI risk management into your broader compliance and data protection frameworks. Essentially, it’s about making sure that AI systems contribute positively to your operations without compromising on legal or ethical standards.

Why this matters now—and in the future

Even though the full enforcement of the EU AI Act will be staggered over the next few years, starting early with compliance will give your business a strategic advantage. This will help you avoid legal troubles and position you as a leader in responsible AI use—a critical factor as consumers and partners become increasingly aware of AI's implications.

Related: Learn how the EU AI Act impacts AI products or services and discover top tips on risk classifications and compliance strategies
 
Stay ahead of AI regulations—download the ultimate guide to the EU AI Act

Four ways to manage AI compliance and security risks

When it comes to AI, staying compliant and secure is essential for protecting your business and maintaining customer trust. Here are four practical strategies to help you stay ahead of the curve and manage AI risks effectively.

1. Establish strong data governance

Good data governance is the foundation of any successful AI project. Do you have clear policies in place for data collection, storage, and use? If not, it’s time to create them.

Action step: Lead the charge in developing a data governance framework that’s robust and up-to-date. Make sure everyone in your organisation knows their role and responsibilities. And don’t forget to review and update your policies regularly to keep pace with changing regulations.

2. Be transparent with your customers

Would your customers be comfortable with how you’re using their data? Transparency is key to maintaining their trust and ensuring compliance with regulations like the GDPR. Customers should always know what data you’re collecting and why, and they must be aware that they can revoke their consent at any time.

If they do revoke consent, your AI system must allow for deleting or anonymising customer data without affecting how the system functions. Even after data is removed, the AI should still provide accurate responses without relying on the deleted or anonymised data.

Action Step: Make sure your privacy notices are clear, concise, and easy to find. Work with your marketing and communications teams to ensure your messaging is consistent and customer-friendly. Additionally, ensure your systems allow for the seamless revocation of consent without compromising the effectiveness of your AI.

3. Build privacy into your AI projects

Privacy shouldn’t be an afterthought—you should build it into your AI projects from the start. This approach, known as "privacy by design," helps you identify and address privacy risks early, rather than scrambling to fix them later.

Action Step: Integrate privacy by design into your AI development process. Collaborate with privacy experts during the planning stages and continuously monitor your AI systems for potential privacy issues. DPIAs are a helpful way to identify compliance gaps, risks and remedial measures that will help you continue to innovate responsibly. You should also consider regular reviews and revisions of your DPIA to incorporate the latest technological developments and keep your risk assessments up to date with what's state of the art.

4. Conduct regular audits and assessments

Are you regularly checking your AI and data privacy practices? Regular audits and assessments help you catch potential issues before they become big problems. It’s about being proactive, not reactive.

Action Step: Schedule regular audits of your data practices and consider bringing in third-party experts for an unbiased assessment. Use what you learn to strengthen your data governance and compliance efforts.

Make privacy a day-one priority when creating AI products and services

Using personal data to train AI can be a double-edged sword for businesses like yours. On one side, there are huge benefits—AI can drive innovation, improve customer experience, and streamline operations. But there are risks, too—like data breaches, legal penalties, and the potential loss of customer trust. 

For DPOs, CEOs, and heads of legal, the message is clear: AI offers a competitive advantage, but only if you implement it responsibly. By focusing on compliance and customer trust, you can leverage AI to drive innovation and growth while safeguarding your company's reputation and bottom line... 

AI Data Privacy and Information Security

Want to learn more about the security and compliance implications when creating and using AI products? We can help. Get in touch with a member of our team to talk about how you can balance innovation with compliance. 

Frequently asked questions

What is AI in compliance?

AI in compliance refers to the use of artificial intelligence to automate, streamline, and enhance regulatory compliance processes. AI tools can assist in tasks such as risk assessment, data processing, monitoring for compliance violations, and generating reports, making it easier for organisations to meet legal and regulatory requirements. These tools can also improve efficiency by reducing manual work and ensuring that compliance processes are more accurate and up to date. 

What is the UK compliance for AI? 

In the UK, AI technologies must comply with various regulations, including the UK GDPR, Data Protection Act 2018, and other sector-specific laws. The UK government has also introduced a pro-innovation framework to regulate AI, focusing on transparency, fairness, and accountability. Organisations deploying AI must ensure that it respects data privacy rights, avoid biases in decision-making, and maintain accountability for how AI systems are used. 

What is AI for GDPR compliance?

AI for GDPR compliance involves using AI technologies to assist with tasks related to the General Data Protection Regulation (GDPR). This includes automating data subject requests, identifying personal data, monitoring data transfers, and ensuring that data processing activities comply with GDPR principles like transparency and data minimisation. AI can also help organisations keep records of processing activities and mitigate privacy risks, but it must be designed in a way that complies with GDPR requirements.

Will compliance be replaced by AI?

While AI can significantly enhance and automate many compliance tasks, it is unlikely to replace human-led compliance entirely. AI can process large volumes of data, detect patterns, and assist in decision-making, but it still requires human oversight to interpret results, address ethical concerns, and manage areas where judgment or legal expertise is essential. AI will continue to play a supportive role, helping organisations meet compliance obligations more efficiently while human experts focus on strategic and complex compliance decisions.