Multi-tenant artificial intelligence (AI) workloads deployed on Kubernetes clusters within OpenStack infrastructure face significant network performance degradation and security isolation challenges. This paper examines network overhead optimization strategies and secure isolation mechanisms for Kubernetes–OpenStack environments hosting data-intensive AI pipelines. Building on foundational work by Patchamatla (2018), which identified Neutron overlay networking overheads as critical bottlenecks, this study conducts a comprehensive comparative analysis of Container Network Interface (CNI) plugins, including Calico, Cilium, Flannel, and Single Root I/O Virtualization (SR-IOV), evaluating their performance–security trade-offs in multi-tenant contexts. The research synthesizes empirical findings from performance benchmarks, security analyses, and architectural evaluations. Key findings reveal that eBPF-based CNI implementations (Calico, Cilium) reduce packet-path latency by 40–60% compared to iptables-based alternatives while maintaining robust network policy enforcement. SR-IOV configurations achieve near-native throughput improvements of approximately 30% but sacrifice orchestration flexibility. Zero-trust architectures implemented through workload identity verification and per-packet tagging demonstrate 2–5× lower packet latency with reduced CPU overhead compared to traditional perimeter security models. The paper proposes a layered isolation framework combining namespace-level policy enforcement, runtime verification, and hardware-accelerated networking to balance performance requirements with security guarantees. Three comparative performance tables illustrate CNI plugin benchmarks, isolation mechanism trade-offs, and security enforcement overheads. This work contributes actionable architectural guidance for deploying production-grade multi-tenant AI pipelines on Kubernetes–OpenStack platforms while addressing the dual imperatives of computational efficiency and tenant isolation.