Especially in times when non-human actors, such as AI, are being granted ever greater autonomy, security aspects must be taken seriously in their entirety and can no longer be excluded. In various areas, it is therefore important to ensure that processes can be carried out in compliance with data protection regulations and are sufficiently secure.
Business is largely based on trust: trust on the part of customers in the added value that the respective company is able to deliver, trust on the part of employees in continuing continuous employment, trust on the part of society in legally secure and data protection-compliant procedures.
The use of AI in a company must therefore adapt to the status quo. The unquestionable disruptive potential of AI should be tamed for this purpose.
Key aspects of AI-related data protection
The question of the potential added value of implementing AI mechanisms should always include detailed consideration of adequate process routines. With regard to data protection, the main aim is to proceed in a legally secure manner and not to recklessly jeopardize existing trust. In terms of data collection and storage, this means in particular that it must be clearly communicated which data is collected and stored in which way in order to significantly support the AI process. After all, the GDPR always applies to all questions of data protection, and you should never forget that.
“Where people work, people make mistakes”
This simple saying shows that even a system based on purely human labor is not 100% secure. In the event of misconduct, you can, as it were, make a more or less clear description of responsibilities. Once an AI has been implemented, has completed its first iterative learning cycles independently, such an assignment of responsibility is no longer easily possible! Accordingly, it is essential to address security and data protection issues even before the actual integration of AI.
At the latest when AI systems become individual actors, emancipate themselves from their existence as mere tools, it must be ensured that procedures involving sensitive data sets are subject to a reliable quality standard. Mistakes can have disproportionately far-reaching consequences in this regard, as they refer to a systemic failure and are not simply attributed to an individual actor. Because even though AI is (semi-) autonomously circling, it is only doing so for now on the premise of a leap of faith from human employees — it must prove itself! It is both about maintaining existing trust and generating new forms of trust. Especially in times when AI still has the taste of what may be quite short-lived hype, it is important that, if you believe in the benefits of AI-based automation, an exemplary implementation is suggested.
Conclusion
Ultimately, every form of AI integration is about keeping an eye on people and/or users, i.e. never seeing AI as an end in itself. Implementing routine machine learning can help to complete repetitive tasks more accurately than can be done by human actors. A current AI-supported system does not become operationally blind, cannot be distracted and cannot actually be described as truly “intelligent.” Basically, it is about setting the course today for an approaching future, in which there is no longer a need to talk about human employees, but in which AIs equipped with special domain knowledge can also make an important contribution to intellectual topics. By then at the latest, adequate data protection should be the guardrail for every decision to be made.
