27.11.2024

Proof of concept for artificial intelligence

Generative AI: building practical expertise while accounting for regulatory requirements and secure implementation 

Generative AI models offer a wide range of possible applications and have the potential to significantly advance digitalization. However, in order to make a realistic assessment of the possibilities and limitations of this technology, it is essential to build up practical expertise. This can be done by implementing proof-of-concepts (PoCs) for smaller, non-critical use cases. It is crucial to observe regulatory requirements and ensure secure implementation.

Regulatory framework for AI

With the AI Act, the European Union has passed the world's first comprehensive law on the regulation of artificial intelligence. This law follows a risk-based approach in which AI systems are subject to different requirements depending on their risk potential. High-risk AI systems, such as those used in safety-critical infrastructures or in the healthcare sector, must meet strict requirements. These include requirements for transparency, human oversight and risk management.

Safe implementation of AI systems

When introducing AI systems, companies should follow the principle of "secure by design". This means that security aspects are integrated into the development process right from the start. Important steps here are

  1. Threat analysis: Identification and evaluation of potential threats.
  2. Security requirements: Definition of specific security requirements for the AI system.
  3. Implementation of security measures: Introduction of technical and organizational risk mitigation measures.
  4. Regular review and updating: Continuous evaluation of the system and adaptation to new threat situations.

The risk of security gaps and attacks can be minimized by taking security aspects into account at an early stage.

Proof of concepts taking regulatory and security aspects into account

Companies should consider the following points when implementing PoCs for generative AI:

  • Regulatory compliance: Ensure that all relevant legal requirements are met as early as the planning phase.
  • Data protection: Ensure that the data used complies with data protection regulations and that no personal data is processed without appropriate authorization.
  • Transparency: The functioning of the AI system should be comprehensible and explainable in order to create trust among users and stakeholders.
  • Risk management: Implement a comprehensive risk management system that identifies and assesses potential risks and defines suitable measures to mitigate them.

Conclusion

Developing practical expertise in dealing with generative AI models is essential in order to fully exploit their potential. By implementing proof-of-concepts, companies can gain valuable experience. However, it is essential to observe regulatory requirements and ensure secure implementation in order to minimize risks and promote trust in the technology.

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