Sarcouncil Journal of Multidisciplinary

Abstract: The integration of artificial intelligence systems with edge computing infrastructures presents unprecedented security challenges that traditional protection mechanisms cannot adequately address. Edge-deployed AI systems operate across geographically dispersed nodes with varying trust levels, creating complex attack surfaces vulnerable to specialized threats, including model poisoning, adversarial perturbation, and distributed inference manipulation. This content explores how segment routing technology combined with microsegmentation principles creates robust security architectures specifically designed for distributed AI environments. The combination establishes workload-specific security boundaries that isolate individual AI components based on risk profiles and data sensitivity levels while maintaining performance requirements critical for time-sensitive applications. Through programmable network paths and granular traffic control policies, organizations can implement zero-trust security models across fluid network boundaries while preserving the low-latency characteristics essential for edge intelligence operations. The framework demonstrates exceptional effectiveness in preventing lateral movement, containing security incidents, and protecting sensitive AI assets while introducing minimal operational overhead. As edge computing continues transforming AI deployment architectures, this security framework addresses urgent protection requirements for distributed intelligence systems operating beyond traditional security perimeters.