Description: Surveillance monitoring is the process of continuously observing and evaluating the performance and anomalies of a system. This approach is used to ensure that systems operate efficiently and effectively, allowing for early identification of issues that could affect their functioning. In the context of simulation with artificial intelligence (AI), surveillance monitoring becomes a crucial tool for analyzing the behavior of models and algorithms, ensuring they perform as expected under various conditions. The main characteristics of this process include real-time data collection, pattern analysis, and generating alerts for any detected anomalies. The relevance of surveillance monitoring lies in its ability to enhance decision-making, optimize resources, and minimize risks, which is especially important in complex and dynamic environments where AI is applied. As technologies advance, surveillance monitoring has become more sophisticated, integrating machine learning techniques that allow systems to learn from their own experiences and adapt to new situations, thereby improving the accuracy and effectiveness of monitoring.
