Trust scoring algorithms for zero trust-based software-defined perimeter architectures: A systematic literature review of advancements, challenges, and future directions

Abstract

Trust score algorithms (TSAs) form the core of dynamic access control in zero-trust architectures (ZTA); however, their development remains fragmented, with persistent challenges in standardization, scalability, and validation. This systematic literature review (SLR) consolidates research on TSAs used in adaptive and zero-trust (ZT)–oriented security systems. A total of thirty-three (33) peer-reviewed papers, mostly published up to December 2024, are reviewed and analyzed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant selection approaches. Our analysis focuses on four key themes: (i) challenges in architecture and implementation; (ii) approaches to context-aware trust estimation; (iii) differences between adaptive and static trust models; and (iv) common limitations found across the existing research. Learning-driven TSAs that aggregate heterogeneous contextual signals, including device posture, behavioral indicators, and external threat intelligence, are frequently reported to outperform static models. However, about 67% of the reported studies entirely rely on simulation-based evaluations. Moreover, fewer than 20% of reported works consider adversarial threat scenarios. Only a small portion tests scalability beyond 1000 nodes, which limits real-world conditions. The pervasive use of custom, non-standardized metrics further impedes cross-study comparison and synthesis. From the synthesized evidence, this review derives a taxonomy of TSAs and a conceptual adaptive trust orchestration framework. The findings emphasize standardized, realistic attack evaluations, transparent trust calculations, and cross-domain validation to enable reliable large-scale deployment of ZT security systems.