AI-Integrated Zero Trust Architectures: A Socio-Technical Analysis of Unified Enterprise Cybersecurity Platforms

This study investigates how integrated cybersecurity platforms, when combined with Artificial Intelligence (AI) and Zero Trust Architecture (ZTA), enhance enterprise cyber defense capabilities. Adopting a qualitative content analysis of secondary sources—including peer-reviewed academic literature, industry analyst reports, international standards, and vendor white papers—the research examines three dimensions: (i) improvements in threat detection, response efficiency, and operational resilience; (ii) the role of AI in automating and augmenting security operations; and (iii) governance challenges arising from enterprise-scale AI adoption. Evidence across the reviewed sources indicates significant reductions in mean time to detect and respond (MTTD/MTTR), lower false-positive rates, and improved breach containment enabled by continuous verification and micro-segmentation. However, the findings also highlight that AI introduces new systemic risks, such as model poisoning, model inversion, and opaque decision-making, which necessitate robust explainability, auditability, and sustained human oversight. To address these dynamics, the study advances a socio-technical perspective in which AI-enabled security platforms are embedded within Zero Trust principles, governed through structured AI management systems, and supervised by skilled practitioners. The paper contributes a conceptual foundation for designing resilient, accountable, and human-centered AI-augmented cybersecurity architectures.)