AI-driven Vulnerability Triage in Connectivity Constrained Environments: A Taxonomy, Systematic Literature Review and Future Directions

Sovereign cloud environments in critical infrastructure deployments face increased adversarial threats across software delivery pipelines and air gapped operations. The challenges are further compounded by terrestrial and non terrestrial network (TN-NTN) segments. This systematic literature review (SLR) investigates the intersection of secure software delivery, Artificial Intelligence (AI) vulnerability triage, and DevSecOps within the constraints of TN-NTN and air gaps. For this survey, we included 22 studies identified through a two-arm PRISMA 2020 systematic literature review, which originated from 429 database records and 6 snowball citations (2 anchor citations). Additionally, we propose a taxonomy that maps the included studies against a two dimensional framework: (i) AI complexity: classical Machine Learning (ML), hybrid, Generative Artificial Intelligence (GenAI)/ Large Language Models (LLMs) and (ii) connectivity assumptions: air gap, partial non terrestrial (NTN), terrestrial. The analysis of results and outcomes is presented to illustrate research trends in AI-driven vulnerability triage in connectivity-constrained environments. Finally, we offer future research directions and identify open challenges for readers interested in exploring this area further.