In the current fast-paced digital environment, enterprises are striving to offer a seamless and integrated customer experience across multiple touchpoints. This improved experience often leads to higher conversion rates and increased customer loyalty. To deliver such an experience, enterprises must think beyond the traditional boundaries of their architecture. The architecture of the digital ecosystem is expanding and becoming more complex, achieved either by developing advanced features in-house or by integrating with third-party solutions, thus extending the boundaries of the enterprise architecture. This complexity poses significant challenges for both observability and security in a digital ecosystem, both of which are essential for maintaining robust and resilient systems. Observability entails monitoring and understanding the internal state of a system through logging, tracing, and metrics collection, allowing organizations to diagnose performance issues and detect anomalies in real time. Meanwhile, security is focused on protecting sensitive data and ensuring service integrity by defending against threats and vulnerabilities. The data collected through these observability practices can be analyzed to identify patterns and detect potential security threats or data leaks. This paper examines the interconnections between observability and security within digital ecosystem architectures, emphasizing how improved observability can strengthen security measures. The paper also discusses studies conducted in the AI/ML field aimed at enhancing security through the use of observability. These studies explore how advanced machine learning techniques can be applied to observability data to improve security measures and detect anomalies more effectively.