Using AI securely in companies: An overview of important data protection and GDPR aspects
In today's business world, the importance of artificial intelligence (AI) is rapidly increasing. From customer service to product development to business process optimization, companies in all industries recognize the potential of AI to drive innovation and create more efficient processes. But with this technological revolution comes new challenges, particularly with regard to data protection.
Data is at the heart of every AI. Successful use of AI requires large amounts of data to train, test, and optimize models. But where does this data come from? It is often personal information from customers, employees or business partners. And this is where it gets complicated. Because while companies strive to take advantage of AI, they must also ensure that they do not infringe the data protection rights of data subjects. Keyword: AI data protection.
Die General Data Protection Regulation (GDPR) The European Union has significantly changed the landscape of data protection. It sets strict guidelines for processing personal data and requires companies to integrate data protection into their processes from the outset. This also applies to AI projects in particular, as they are often based on the analysis and processing of large amounts of data.
In this article, we'll dive deeper into the key privacy and GDPR issues to consider when implementing AI in companies. The aim is to provide a clear overview and guidance to ensure that their AI initiatives are not only innovative but also compliant with data protection regulations.
Basics of the GDPR
The General Data Protection Regulation, better known as GDPR or GDPR, is a key set of rules in the European Union that standardizes the handling of personal data in the member states. Since its introduction in 2018, the GDPR has radically changed the way companies collect, store, and process data.
What is the GDPR and why is it important?
The GDPR is intended to protect the personal data of EU citizens. It gives individuals more control over their data and ensures that companies are more transparent about how and why they process data. For companies, the GDPR not only imposes stricter data protection standards, but also significant penalties for non-compliance — with fines that can amount to up to 20 million euros or 4% of annual global turnover.
The GDPR is based on seven basic principles that guide the handling of data:
Lawfulness, processing in good faith, transparency:
The lawfulness of data processing is one of the fundamental principles of the GDPR. Companies must ensure that they have a clear legal basis for processing personal data. This may be the consent of the person concerned, the fulfilment of a contract or compliance with a legal obligation.
Good faith processing means that companies should incorporate ethical considerations into their data processing practices and should not use misleading or fraudulent tactics.
Transparency requires companies to provide clear, understandable, and easily accessible information about data processing. This is particularly important when using AI, as algorithms are often complex and difficult for laymen to understand.
Purpose limitation and data minimization:
Purpose limitation means that personal data may only be collected for specific, expressly stated and legitimate purposes. The data must not be further processed in a way that is incompatible with these purposes.
Data minimization requires companies to collect and process only the absolutely necessary data for the intended purpose. In the context of AI, this is particularly critical, as AI models tend to process large amounts of data.
Accuracy and memory limit:
Accuracy means that personal data must be accurate and up to date. Organizations must take steps to correct or delete inaccurate data without delay.
Storage limitation refers to the fact that personal data may only be stored for as long as is necessary to achieve the processing purposes. This requires regular checks and, if necessary, the deletion of data.
Integrity, Confidentiality, and Accountability
This principle requires companies to take appropriate security measures to protect personal data from unauthorized access, loss, or destruction. This includes technical and organizational measures such as encryption, regular security checks and access controls.
Companies must not only comply with GDPR principles, but must also be able to prove that they are doing so. This requires extensive documentation of data processing activities, including security measures taken, processing purposes and storage periods. Accountability also requires risk assessments to be carried out and data breaches reported.
These principles are particularly relevant for AI initiatives, as AI systems are often based on large amounts of data and carry out complex data processing activities. An understanding of the GDPR and its requirements is therefore essential for any company planning to integrate AI technologies into its processes.
Data protection risks when using AI
With the rapid rise of artificial intelligence in the DACH region and beyond, data protection concerns are also increasing. AI systems are data-hungry and often rely on the availability of sufficiently large amounts of (personal) information when processing the data. This not only raises questions about GDPR compliance, but also concerns topics such as data integrity, security, and storage.
Data collection and processing: Potential threats
Collecting and processing large amounts of data is essential when using AI. But what happens when that data is incorrect, biased, or out of date? Misinterpretations can lead to undesired business decisions or even have legal consequences.
Companies in the DACH region must ensure that their data sources are reliable and ethically justifiable. Regular reviews and, if necessary, processing of the data help the AI models to work in the best possible way, which ultimately also has a positive effect on the company's success.
Artificial intelligence can often extract more from data than meets the eye. This may result in unintentional disclosure of sensitive information. It is therefore essential for companies to comply with strict data protection guidelines and carry out regular data protection impact assessments. This should be considered and implemented accordingly as soon as artificial intelligence is implemented into business processes.
Data hosting: Europe vs. USA
Another critical point in data protection is where the information is stored and processed. This is particularly relevant for companies in the DACH region, as EU data protection standards are among the strictest worldwide. Many companies rely on European hosting solutions (including using AI) to ensure that they comply with GDPR requirements.
In contrast, US servers often have different data protection regulations that do not always comply with European standards. The famous “Privacy Shield” agreement between the EU and the USA, for example, was declared invalid by the European Court of Justice, which further complicates data transmission across the Atlantic. Companies that target the DACH market should carefully consider where and how their data is hosted and processed.
GDPR requirements specific to AI
Artificial intelligence in companies has great potential, but also specific challenges with regard to the General Data Protection Regulation (GDPR). It is of central importance for companies in the DACH region to understand and implement these requirements in order to comply with data protection regulations.
1. Transparency and information requirements
One of the core principles of the GDPR is transparency. Companies must provide data subjects with clear and understandable information about how their data is being used by artificial intelligence. This also includes explaining how the AI model works and clarifying how decisions are made, particularly when they are automated.
2. Data minimization and purpose limitation
Artificial intelligence is often dependent on large amounts of data for optimal work, but the principle of data minimization applies. Data may only be collected and processed to the extent necessary for the specified purpose. Companies must ensure that their AI models are only fed with the data they really need and are not overloaded with superfluous information.
3. Automated individual decisions and profiling
A critical point in the context of artificial intelligence and GDPR is the automated making of decisions without human intervention. The GDPR provides that individuals have the right not to be subject to a purely automated decision that has legal effect on them or similarly significantly affects them. Companies must therefore implement mechanisms that enable those affected to request a human review of such decisions.
4. Obligations of the data processor
When external service providers are used for AI development or implementation, data protection contracts must ensure that they too meet the requirements of the GDPR. This is particularly important if the service provider or provider of AI services is based outside the European Economic Area (EEA).
5. Accountability and documentation
The GDPR requires companies to be able to document their data processing activities and prove that they comply with data protection principles. This is particularly important in the context of AI, as algorithms often have complex and difficult to understand decision-making processes. Detailed documentation of these processes is therefore essential.
6. GDPR compliance from the start (“Privacy by Design”)
A proactive approach is key to GDPR compliance for AI projects. Data protection should not be integrated into the development process of AI systems as an afterthought, but from the outset. This means taking privacy-friendly technologies and processes into account right from the design phase and ensuring that AI models comply with the data protection principles of the GDPR.
Best practices for AI and data protection
In the constantly changing landscape of artificial intelligence (AI) and data protection, best practices are crucial, particularly for companies in the DACH region, which must comply with the strict requirements of the GDPR. Here are some best practices that companies should follow when implementing AI projects:
1. Data processing: anonymization and pseudonymization
The correct preparation of data plays a central role in GDPR compliance. Anonymization means changing personal data in such a way that the persons concerned can no longer be identified.
Pseudonymization, on the other hand, replaces personal data with pseudonyms, so that attribution is no longer possible without additional information. Both methods can help minimize the risk of data breaches while providing valuable data for AI systems.
2. Use of data protection impact assessments (DSFA) for AI projects
DSFAs are a systematic method for identifying and minimizing data protection risks in data processing. In the context of artificial intelligence, they can help identify potential problems at an early stage and take appropriate countermeasures. This is particularly important when the use of AI relates to sensitive or personal data.
3. Establishment of a data protection officer
A data protection officer monitors compliance with the GDPR and other data protection regulations in the company. He is the point of contact for privacy-related questions and ensures that best practices are implemented and adhered to in the company. Especially when using AI, a data protection officer can offer valuable advice and support.
4. Hosting: Search for solutions in Europe for increased data security
The issue of hosting AI applications is of particular importance in Europe, particularly in the DACH region in the context of data security and the processing of personal data. With a focus on data protection and GDPR compliance, many companies are looking for hosting solutions within Europe. Microsoft Azure is one example of a large provider that now hosts AI models such as GPT in Europe, which provides companies with additional security in terms of data protection.
On-premise solutions, where companies use their own servers and infrastructure, are another option. They offer the advantage that the company has full control over the processing and storage of data. However, the costs for on-premise solutions are often significantly higher, as both the purchase, operation and maintenance of the infrastructure must be borne by the company itself.
Finally, any company that wants to implement artificial intelligence in Europe should carefully consider which hosting solution best suits their requirements. For the efficient use of AI, not only the question of costs, but also data security and GDPR compliance must be considered.
Conclusion and outlook
The introduction of artificial intelligence in companies, particularly in the DACH region, offers enormous opportunities for innovation and efficiency. At the same time, it presents companies with complex data protection challenges, particularly in light of the GDPR. While AI models are able to analyze huge amounts of data and gain valuable insights from it, companies must ensure that they respect the rights and privacy of data subjects in terms of storing and processing the data.
In this article, we have examined the basics of the GDPR and its specific requirements for AI projects. It became clear that transparency, data minimization and compliance with the basic principles of data protection are of central importance. Choosing the right hosting for AI applications is just as important, taking into account both the benefits of European hosting solutions and the costs of on-premise solutions.
The data protection landscape will continue to develop, particularly in light of rapid advances in AI technology. Companies must therefore remain alert, keep themselves informed about new developments and regularly review and adapt their data protection practices. What is certain is that the term “AI regulation” will appear more frequently in the next few years.
AI and data protection don't have to be at odds with each other. With a well-thought-out strategy, the right advice, and a proactive approach, companies can take advantage of AI while meeting data protection requirements.
The future will undoubtedly bring further innovations and challenges. However, companies that invest in privacy-compliant AI investments today will be better equipped to overcome these challenges and take full advantage of AI opportunities.
