Atom’s senior cyber security consultant, Daniel Baird, explains how AI is changing the game for businesses and making data protection an increasingly complex issue.
Artificial Intelligence (AI) is rapidly transforming the way businesses operate. Whether it’s automating customer service, providing insights into logistics improvements or even detecting fraud, AI is enabling us to make improvements across almost every aspect of business.
But as AI becomes more embedded in everyday systems, it raises serious questions about how data is collected, processed and protected.
For organisations new to AI, data protection can feel overwhelming. However, rather than avoid the topic, getting it right from the start is essential for businesses who want to avoid headaches (and costly legal issues) further down the line.
After all, protecting staff, customer and supply chain data is paramount for building trust, reducing risk and making smarter use of technology.
In this beginner’s guide, I want to break down what you need to know and share some practical steps to strengthen your data resilience.
Why does AI cause data protection issues?
Fundamentally, AI feeds off data. The whole premise of Artificial Intelligence as it currently exists is that information is scraped from an almost unimaginably large array of data sources and then that information is aggregated in order to deliver solutions.
While this mining of information can have hugely positive results, there are also concerns about the nature of the data that is being fed into these learning machines – in particular, the use of personal data without a clear purpose.
It has become apparent in recent months that AI technologies depend heavily on personal data – analysing patterns among users in order to make decisions. While this can certainly help to deliver improved service – e.g. social media using user habits to inform the content that is served to them in the future – the fact that data is being used without users’ explicit consent is a contentious issue.
Perhaps more worrying, however, is where data that is being shared with AI tools is being held, who has access to it and how that data might be used in the future. This is of particular concern in instances where software applications plug in open source AI solutions in order to enhance performance.
Why? Because while the application itself may be careful about how, when and why it uses your personal data, it also leans on a technology that can harvest data and share it elsewhere without any transparency.
Being open source, any threat actor able to identify a vulnerability can also easily access that software – or any other using the same AI tool.
AI can leave you flouting GDPR
Under the UK GDPR, data protection principles still apply – even when AI is involved. Under the UK GDPR, organisations must ensure that personal information is used responsibly and in a transparent way. What’s more, businesses are required to implement robust data protection measures to ensure that this data does not fall into the wrong hands.
Practical steps to improve AI data resilience
Whether you’re using third-party AI tools or developing your own models, here are some actions you can take:
Conduct a Data Protection Impact Assessment (DPIA)
- Before launching any AI project, carry out a DPIA to identify potential risks and demonstrate accountability. This is particularly important if the system involves profiling or automated decision-making.
Map and minimise your data
- Only collect the data you need.
- Anonymise or pseudonymise personal data where possible.
- Regularly audit your datasets to ensure they’re relevant, accurate and up to date.
Choose transparent and explainable AI tools
Whenever possible, opt for models that allow insight into how decisions are made. This helps build trust and meet your GDPR obligations for transparency and fairness.
Build in access controls and encryption
Ensure AI systems have:
- Role-based access controls
- Strong authentication
- End-to-end encryption for data in transit and at rest
These measures help reduce the impact of a breach and show good data governance.
Monitor for bias and discrimination
Regularly test AI outputs for unintended bias. This could involve:
- Diverse training data
- Human oversight of decisions
- Clear procedures to allow individuals to contest AI-based outcomes
Train your team
Everyone involved in handling AI or personal data—developers, analysts, marketers—should understand their data protection responsibilities. Ongoing training is key.
AI can drive incredible efficiencies and insights. But it’s not without its risks. Taking a proactive, responsible approach to data protection will help you unlock AI’s potential without compromising privacy or compliance.
Want to avoid security missteps as you begin to embrace the power of AI? Get in touch with us at Atom and let’s have a chat. Contact us
