One of the most important is privacy protection in line with the GDPR. European legislation sets high demands on privacy policies when processing data, a requirement that can conflict with the use of public AI tools if you do not have the right cloud infrastructure.

In this article, you will learn how to interpret complex GDPR guidelines and to what extent they relate to the use of AI tools. We will also show you how to avoid common mistakes and safely use the power of AI within the framework of the GDPR with a private cloud infrastructure. Take advantage of it!

The GDPR is a large piece of European legislation. Its main features and more detailed provisions are based on a number of important, overarching principles and principles. These are:

Many AI-specific applications, such as large-scale data processing, data collection and automated decision-making, bring additional risks for proper GDPR compliance. AI systems work with large amounts of data. It is a challenge to properly classify and segment this mountain of data in order to create a clear, quickly accessible overview internally about the sensitivity level of specific datasets.

Furthermore, organizations are often unaware of how public AI tools, hosted on external servers, may share or process data in a way that violates GDPR guidelines. This often leads to risks when using public cloud and AI services. Suboptimal training of AI models increases the risk of systems introducing biased decisions or facilitating profiling. AI applications that are not sufficiently secured are vulnerable to manipulation by hackers looking to mislead humans.

Non-compliance with the GDPR leads to a significant risk of sky-high fines (4% of total annual turnover or a maximum of 20 million euros) and reputational damage. It is therefore essential that organizations that process personal data with AI systems are extra careful with artificial intelligence.

There are several things that can go wrong in terms of compliance if you don’t use AI solutions thoughtfully enough. For example, many organizations use public AI tools without knowing exactly where their data is going or how it is being processed, an error or carelessness that can easily result in GDPR breaches.

Another potential pitfall is the processing of personal data by third parties. Think of public AI tools that process personal data in ways that violate the GDPR, for example by using international data centers that do not have the security and facilities required by European privacy legislation.

The digital infrastructure you use to store, share and exchange privacy-sensitive data largely determines how 'GDPR-proof' your organization is. A private cloud infrastructure can prevent a lot of non-compliance suffering.

In a private cloud environment, you have full control over where your data is stored and how it is processed. The result is obvious: more options to keep a grip on compliance with GDPR requirements such as data localization and security.

You can design AI systems within a private cloud to automatically comply with GDPR guidelines. Data minimization and good monitoring of data processing help you with this.

A private cloud provides a strong foundation for 'privacy by design' and 'privacy by default'. The former involves paying attention to data protection in the design phase of a service or product, while privacy by default means that the default settings are privacy-friendly. Because you are in control in a private cloud and are not dependent on decisions from a public cloud provider when processing and managing personal data, you have optimum control over your compliance policy.

But what are the most important steps you need to take if you want to use innovative AI solutions that are in line with the guidelines and high demands that the GDPR places on data and privacy protection?

The following step-by-step plan will guide you on the right path:

1. Perform a thorough data audit. This ensures that you know what data (types) you are using and how you are processing that data.

2. Choose a private cloud to host AI applications. With a secure, carefully designed private cloud infrastructure, you keep full control over your data.

3. Integrate transparency and consent into your AI solutions by ensuring all data processing is done transparently and you have appropriate consent from users to process their personal data.

4. Compliance monitoring. Use analytics tools and dashboards to monitor AI usage. This way, your data policy remains GDPR-compliant in the long term.

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Reaping the benefits of AI innovation while remaining GDPR compliant is a challenge that every organization that processes personal data has or will face. Fortunately, the combination of a robust private cloud infrastructure, continuous monitoring of data processing processes and solid GDRP knowledge offers sufficient tools to prevent compliant from turning into complAInt.

Uniserver realizes that more and more companies are using AI solutions to optimize processes and convert data into valuable insights. That is why we will soon introduce a new service hosted in our own private cloud environment that allows you to safely use AI functionalities on your own datasets. This way you can easily set up your own 'ChatGPT interface' without being dependent on public cloud providers such as Microsoft, Amazon or Google.

The service is ideal for companies that demand maximum data security, but still want to benefit from the rich arsenal of possibilities that AI offers. Want to know more? Please feel free to contact us or subscribe to our newsletter to stay informed.