Converging Zero Trust and IoT Security: A Multivocal Literature Review
- URL: http://arxiv.org/abs/2604.24205v1
- Date: Mon, 27 Apr 2026 09:08:22 GMT
- Title: Converging Zero Trust and IoT Security: A Multivocal Literature Review
- Abstract summary: The convergence of Internet of Things (IoT) security and Zero Trust (ZT) principles is a trending topic, demanding a comprehensive, multi-perspective analysis.<n>We present the first multivocal literature review (MLR) on this topic, combining 68 academic and 36 industrial studies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The convergence of Internet of Things (IoT) security and Zero Trust (ZT) principles is a trending topic, demanding a comprehensive, multi-perspective analysis. We present the first multivocal literature review (MLR) on this topic, combining 68 academic and 36 industrial studies. This comprehensive review identifies two complementary yet divergent perspectives: academia focuses on IoT compliance with ZT principles through IoT modifications, while industry prioritizes practical integration within existing ZT frameworks guided by NIST standards. The analysis reveals critical research gaps in socio-technical understanding, cost-benefit evaluation, and interdisciplinary collaboration, highlighting these as key directions for future research.
Related papers
- A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasms [0.0]
This paper conducts a critical analysis of tools and trust mark frameworks intended to operationalize trustworthy AI (TAI)<n>Our findings show a strong emphasis on fairness, transparency, and robustness, with comparatively little attention paid to explainability, digital security, and environmental sustainability.<n>We argue that bridging the persistent chasm between AI principles and practice requires expanding ethical objectives, embedding ethics across the AI lifecycle, and fostering broader multi-stakeholder participation.
arXiv Detail & Related papers (2026-07-16T21:56:43Z) - Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges [60.18620798072947]
Large Language Models (LLMs) have been extensively integrated into recommender systems.<n>LLMs offer unprecedented opportunities to enhance trustworthiness.<n>But they simultaneously introduce new risks, such as novel forms of bias and hallucination-induced issues.
arXiv Detail & Related papers (2026-05-30T05:14:53Z) - Rejoinder: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review [49.43514488610211]
This article is the rejoinder to The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review''<n>We organize our response around four core themes: (i) formulating peer review as a statistical estimation problem; (ii) mitigating equity and strategic concerns in the deployment of the Isotonic Mechanism; (iii) incorporating complementary signals such as reviewer rankings and structured metadata; and (iv) exploring a human-centered framework for peer review in the era of generative AI.
arXiv Detail & Related papers (2026-05-24T17:06:47Z) - The Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review [0.0]
This paper presents a systematic literature review to examine the challenges of balancing AI compliance and technological innovation across critical infrastructure sectors.<n>The study identifies three interrelated challenges: fragmented regulations, excessive compliance burdens for smaller to medium enterprises (SMEs), and misaligned governance models.<n>To address these challenges, the study highlights practical governance strategies, including risk-tiered regulation, compliance by design, and explainable AI, to support scalable and trustworthy AI deployment in critical sectors.
arXiv Detail & Related papers (2026-05-09T02:40:28Z) - Zero Trust in the Context of IoT: Industrial Literature Review, Trends, and Challenges [0.0]
The Zero-trust (ZT) model is an increasingly popular model that relies on the idea that no trust should be granted to any entity (network, persons, devices) by default.<n>ZT model is gaining attention from both research and practice, with various levels of adequation between research developed and real-life applications.<n>An example of such an unspecified field is the integration of IoT/Smart Peripheral Devices (SPD).<n>Various reasons explain this gap: specificities of such resources (possibly lower energy/computation power), their lifecycle, and their use, strongly depending on the use of the whole platform
arXiv Detail & Related papers (2026-04-07T07:02:17Z) - Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics [6.078202027484749]
This paper presents a novel, multi-layer, AI-driven analytical framework for mapping research convergence in interdisciplinary teams.<n>The framework integrates large language models (LLMs), graph-based visualization and analytics, and human-in-the-loop evaluation.
arXiv Detail & Related papers (2026-02-26T20:11:33Z) - Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice [11.251046343609078]
This study aims to bridge the gap between theoretical discussion and practical implementation by analysing research trends, definitions of trustworthiness, and practical techniques.<n>We identified 18 different definitions of trust/trustworthiness, with transparency, explainability and reliability emerging as the most common dimensions.<n>We identified 20 strategies to enhance LLM trustworthiness, with fine-tuning and retrieval-augmented generation (RAG) being the most prominent.
arXiv Detail & Related papers (2025-02-27T14:02:33Z) - On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective [377.2483044466149]
Generative Foundation Models (GenFMs) have emerged as transformative tools.<n>Their widespread adoption raises critical concerns regarding trustworthiness across dimensions.<n>This paper presents a comprehensive framework to address these challenges through three key contributions.
arXiv Detail & Related papers (2025-02-20T06:20:36Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [51.26815896167173]
We present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions.<n>Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices.<n>Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - Responsible AI Considerations in Text Summarization Research: A Review
of Current Practices [89.85174013619883]
We focus on text summarization, a common NLP task largely overlooked by the responsible AI community.
We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022.
We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
arXiv Detail & Related papers (2023-11-18T15:35:36Z) - A multidomain relational framework to guide institutional AI research
and adoption [0.0]
We argue that research efforts aimed at understanding the implications of adopting AI tend to prioritize only a handful of ideas.
We propose a simple policy and research design tool in the form of a conceptual framework to organize terms across fields.
arXiv Detail & Related papers (2023-03-17T16:33:01Z) - Revise and Resubmit: An Intertextual Model of Text-based Collaboration
in Peer Review [52.359007622096684]
Peer review is a key component of the publishing process in most fields of science.
Existing NLP studies focus on the analysis of individual texts.
editorial assistance often requires modeling interactions between pairs of texts.
arXiv Detail & Related papers (2022-04-22T16:39:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.