At the same time, many organizations have doubts and concerns about the security of public AI tools such as the now ubiquitous ChatGPT. Is it possible to use such services and still maintain sufficient control over data security and compliance? And what potential risks does using AI solutions in public cloud environments entail? A private cloud is often the key to safely deploying artificial intelligence and taking advantage of the many opportunities that AI innovation offers. Read this article to find out why this is the case.

Modern AI tools have undeniable advantages in automation, data analysis and innovation that can benefit a broad and diverse spectrum of organizations. At the same time, AI innovation in public cloud environments comes with several challenges. Time to take a closer look at the most important ones.

In a public cloud environment you have little control over where and how your data is stored. The cloud provider determines which hardware and software it uses for this and in which data centers your data is stored. This entails potential risks when it comes to business-critical or privacy-sensitive data.

The laws and regulations around data protection, cybersecurity and privacy protection have been tightened and expanded considerably in recent years. Organisations in the Netherlands and Europe must now comply with:

Complying with the above-mentioned laws and regulations is quite a challenge and requires a good overview of all your data and storage locations, a high degree of cyber awareness throughout the organization and excellent, centralized documentation.

When using a public AI application, you train the model based on input from users, among other things. In most cases, you also use this input for the output of another user. Perhaps the biggest challenge when using AI is that many users are not actively concerned with the consequences of sharing privacy-sensitive or business-critical information. Inadequate control over data security quickly leads to risks such as data leaks or non-compliance (fully) with laws and regulations.

In addition, it sometimes happens that information about the AI ​​model is stored outside the EU. Bad business, because then the European laws and regulations no longer apply. This creates a situation that conflicts with the EU AI Act, because it states that everyone (developers, organizations and users) must be able to demonstrate that AI systems comply with European rules.

Public cloud solutions offer unparalleled flexibility and scalability. They also seem cheap in the short term. You don’t have to invest in hardware and you don’t have to deal with high start-up costs. However, managing risks and meeting compliance requirements in a public cloud setting requires a lot of attention and research and monitoring work, which sometimes results in high, unexpected and not always quickly traceable costs.

The private cloud is in many ways the answer to the above challenges. Unlike in the public cloud, in the private cloud as an organization behind the buttons yourself. You keep full control over your data and determine where you put it and who has access to it. Because you store your data locally in a shielded environment, the chance of data leaks is reduced and you can more easily comply with the strict regulations of the GDPR and EU AI Act. The private cloud offers many more possibilities to manage, monitor and expand an AI model than its public counterpart.

A private cloud also offers advantages in terms of performance. Because you do not share hardware and resources with others and your AI models run on dedicated AI hardware, you will rarely or never experience bottlenecks or performance issues, even during extreme peak periods.

What makes the private cloud extremely suitable as a safe place for AI innovation is the high level of security. This puts the public cloud in the shade thanks to the more extensive options for encryption and data isolation. Because you do not share resources with other customers of a cloud provider in a private cloud and can fall back on organization-specific security settings instead of general security measures, there are plenty of opportunities to use AI tools in a way that is in line with the required level of security within your organization and industry. The choice between a private and public cloud does not always have to be a question of either-or. A hybrid cloud landscape that combines the advantages of the two cloud variants into a harmonious whole is an excellent option for many organizations.

The rise of artificial intelligence is unstoppable. A growing number of organizations will structurally fall back on AI technology to optimize processes and convert data into valuable insights. This article shows how important it is to work safely and in a structured manner when exploiting the seemingly endless AI innovation potential.

Uniserver is happy to help you with this and will therefore soon introduce a new service. Thanks to this solution, you can safely use AI functionalities on your own datasets, hosted in our sovereign cloud environment. This means you can effortlessly set up your own ChatGPT interface, without having to rely on public cloud providers like Microsoft or Google. This service is ideal for organizations that demand maximum data security and still want to benefit from the impressive arsenal of interesting AI capabilities.

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