AI and Zero-Trust Architecture for Securing Data in Remote Work Settings: A Comparative Study

The COVID19 pandemic accelerated remote and hybrid work adoption, exposing organizations to insider threats, data breaches, and advanced cyberattacks, which traditional perimeter-based models failed to address; in response, Zero Trust Architecture (ZTA) emerged, and its integration with Artificial Intelligence (AI) has become a cornerstone of cybersecurity strategies by enabling anomaly detection, automated policy enforcement, and rapid incident response. Guided by PRISMA methodology and Rapid review principles, this study systematically examined 25 publications from 2020–2030 across IEEE Xplore, ACM Digital Library, MDPI, SpringerLink, Elsevier, government repositories, and open access archives, applying strict eligibility criteria to ensure methodological transparency and relevance. Findings consistently show that AI-ZTA integration mitigates insider threats, prevents data breaches, and strengthens resilience against advanced cyberattacks, with chronological analysis revealing a progression from foundational frameworks (2020–2023), to risk-oriented literature (2024), applied deployments (2024–2025), and predictive analyses (2025–2026). The review concludes that AI-ZTA is positioned as a critical paradigm for securing decentralized environments, though its long-term success depends on safeguards, workforce training, regulatory compliance, and continuous evaluation mechanisms. This scope and format are consistent with established practices in cybersecurity research, where recent studies have also synthesized fewer than 25 papers through rapid review methods to deliver timely, rigorous, and actionable insights in emerging fields.

Architecture, Comparative Study, Remote Work

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