Sarcouncil Journal of Multidisciplinary

Abstract: The convergence of artificial intelligence and endpoint management has catalyzed the emergence of Autonomous Endpoint Management (AEM) systems that fundamentally transform enterprise security operations. Traditional endpoint management solutions suffer from critical limitations, including periodic scanning gaps, manual intervention requirements, and inability to scale across diverse device populations. AEM addresses these challenges through multi-model stream processing architectures that enable continuous real-time monitoring and automated threat response. The integration of sophisticated machine learning algorithms facilitates predictive threat detection, behavioral analysis, and intelligent remediation workflows that operate at machine speed. Modern AEM platforms seamlessly integrate with enterprise security ecosystems, including unified threat management systems, cloud-native architectures, and IT service management platforms through standardized APIs and event-driven frameworks. These systems process vast telemetry streams through ensemble learning models combining supervised, unsupervised, and deep learning techniques to identify sophisticated threats while maintaining operational efficiency. The implementation of automated workflows extends beyond security to encompass compliance management, vulnerability assessment, and user experience optimization. Enterprise integration patterns utilizing microservices architectures and canonical data models ensure scalability and maintainability while supporting millions of endpoints. The evolution toward autonomous endpoint management represents a paradigm shift from reactive to proactive security postures, enabling organizations to defend against modern cyber threats while reducing operational overhead.