Modeling the Evolution of Risk in AI Systems Throughout their Lifecycle Using the S-сurve

Artificial Intelligence is becoming increasingly embedded in various areas of human life, offering new capabilities that go beyond traditional software systems. Unlike conventional programs that follow fixed instructions, AI can generate its own solutions after processing large volumes of data. However, human input remains essential in designing AI architecture and setting its goals. While AI improves efficiency and decision-making across fields, it also introduces new types of risks. These risks often arise not from malicious intent, but from unpredictable system behavior and user errors. This paper analyzes such risks using a systems perspective and logistic S-curve modeling to examine the AI lifecycle. The analysis shows that the first three stages—development, scaling, and stabilization—carry the highest levels of vulnerability. Key issues include design flaws, insufficient debugging, and lack of continuous monitoring. More advanced systems may evolve through multiple S-curve phases, each introducing new challenges. The study emphasizes the need for stronger legal and ethical standards, drawing on regulatory efforts from the EU, USA, UK, Germany, and France. International cooperation is also highlighted as a key factor in ensuring that AI develops safely and responsibly