AI-Native SDN, Zero Trust, and NGFW Architectures for Autonomous Threat Intelligence in Regulated U.S. Digital Systems

Abstract

This study quantitatively examined the operational effectiveness of AI-native software-defined networking, Zero Trust Architecture, and next-generation firewall systems in strengthening autonomous threat intelligence across regulated U.S. digital infrastructures. The increasing complexity of cyber threats targeting healthcare systems, financial institutions, telecommunications networks, government infrastructures, and critical operational environments has intensified the need for adaptive cybersecurity ecosystems capable of real-time threat detection, predictive analytics, automated response coordination, and intelligent governance management. The study adopted a quantitative experimental research design grounded in cybersecurity risk management theory, defense-in-depth theory, and artificial intelligence decision-making frameworks to evaluate the relationships between integrated intelligent cybersecurity architectures and operational cybersecurity performance outcomes. The final dataset consisted of 150 enterprise cybersecurity infrastructures operating across regulated sectors within the United States. Statistical analysis was conducted using SPSS, R, and Python-based cybersecurity analytics tools to evaluate intrusion detection accuracy, anomaly detection precision, malware containment efficiency, compliance governance performance, operational continuity indicators, and adaptive mitigation responsiveness. The findings demonstrated that integrated AI-driven cybersecurity ecosystems significantly improved operational cybersecurity performance across participating enterprise environments. Intrusion detection accuracy increased from 74.5% within traditional security infrastructures to 93.8% within integrated AI-native environments, while malware containment efficiency improved from 71.3% to 94.5%. Incident response speed demonstrated a 58.4% improvement, with average response times reduced from 8.9 minutes to 3.7 minutes following implementation of integrated AI-native SDN, Zero Trust, and NGFW architectures. Autonomous threat intelligence systems produced the strongest predictive influence on operational cyber resilience with a regression coefficient of β = 0.527 and statistical significance at p = 0.000. Adaptive threat mitigation efficiency reached 91.2%, while operational continuity performance increased by 41.0% across organizations implementing multilayered intelligent cybersecurity ecosystems. The findings further revealed that healthcare, financial, and federal government infrastructures achieved substantial reductions in unauthorized access incidents and improvements in compliance governance performance through centralized orchestration, continuous authentication, and AI-driven behavioral analytics systems.