The role of risk management in AI systems
Within the requirements for high-risk artificial intelligence systems, risk management plays a central role. It is not an isolated control, but a structured process that must be integrated throughout the entire lifecycle of the AI system.
Introduction
Within the requirements for high-risk artificial intelligence systems, risk management plays a central role.
It is not an isolated control, but a structured process that must be integrated throughout the entire lifecycle of the AI system.
This requirement is one of the fundamental elements introduced by the Artificial Intelligence Act and represents one of the main operational challenges for organizations.
What risk management means
Risk management, in the context of the AI Act, involves the identification, analysis and mitigation of risks associated with an AI system.
These risks can concern different aspects, including:
errors in the results produced by the system
negative impacts on fundamental rights
problems related to security or reliability.
The goal is to prevent or minimize these risks before and during the use of the system.
A continuous process
One of the most relevant aspects is that risk management does not end in the development phase.
The regulation requires that this process continues even after the AI system is put into production.
This means constantly monitoring performance, collecting feedback and intervening in case of critical issues.
Conclusion
For many companies, implementing an effective risk management system requires not only technical skills, but also the introduction of new organizational processes.
It is precisely in this step that AI Act compliance becomes a structural and continuous topic, and not an occasional activity.