The recent advancements in artificial intelligence (AI) and machine learning (ML) are significantly transforming various industries, with the banking sector being a prime example. These technologies are revolutionizing decision-making processes, fraud detection, and customer service automation, thereby enhancing efficiency and economic competitiveness. However, the integration of AI also introduces new cybersecurity challenges, such as adversarial attacks that exploit vulnerabilities in machine learning models. To address these risks, there is a growing emphasis on developing secure, resilient, and robust AI models. This shift underscores the need for enhanced cybersecurity frameworks and continuous improvements in defensive mechanisms to safeguard sensitive financial data. Additionally, the concept of security observability is gaining traction, particularly in complex digital ecosystem architectures. By leveraging observability practices, organizations can monitor and understand system behavior, detect anomalies, and strengthen security measures. Advanced machine learning techniques are being explored to analyze observability data, further enhancing security and anomaly detection capabilities. In the realm of distributed systems, innovative methods for aggregating and visualizing trace data are emerging, offering more efficient ways to analyze system behavior and troubleshoot issues. Lastly, there is a notable focus on architecture recovery tools for microservice applications, with a comparison of static analysis tools highlighting their accuracy and potential for combination to achieve higher recovery correctness. These developments collectively indicate a move towards more secure, observable, and efficient systems, driven by advancements in AI, ML, and software engineering practices.