This article examines approaches to autonomous remediation of cyber threats in the context of the development of distributed computing environments, increasing infrastructure complexity, and growing requirements for data sovereignty. The study is conducted as a systematic review and analytical synthesis of scientific publications focused on threat detection methods, decision-making processes, execution of protective measures, and security architectures. Particular attention is given to interpreting the gap between threat detection and remediation as a systemic effect arising from the separation of analytical and execution layers, as well as to analyzing the impact of cloud-centric and virtualized architectures on the speed and accuracy of implementing protective actions. It is established that isolated improvements in detection accuracy do not lead to risk reduction without integrating execution mechanisms into the computational environment. An original architectural model for autonomous threat remediation is proposed, based on localized AI environments, Kubernetes deployed on physical infrastructure, and the implementation of security policies as executable code. The results obtained make it possible to consider the resilience of a security system as a function of execution architecture, degree of localization, and level of integration of all components into a unified control loop. The article will be useful for researchers in cybersecurity and distributed systems, as well as for practitioners involved in designing sovereign and autonomous infrastructures.