International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 3
May-June 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Artificial Intelligence Governance and Cybersecurity Trust: A Comparative Study of Public Infrastructure Systems in India, Singapore, and the United Kingdom

Author(s) Prof. Dr. Dulari Rajput, Mr. A K M Fazlur Rahaman
Country India
Abstract The increasing integration of Artificial Intelligence (AI) into critical public infrastructure systems (e.g., energy, transport, water, digital identity) introduces both operational efficiencies and unprecedented cybersecurity vulnerabilities. However, the relationship between national AI governance frameworks and the cultivation of cybersecurity trust remains underexplored, particularly across divergent socio-technical and regulatory contexts. This paper addresses the central research question: How do different AI governance models in India, Singapore, and the United Kingdom influence cybersecurity trust outcomes in public infrastructure systems?

A comparative multiple-case study design was employed, selecting one major AI-enabled public infrastructure system per country: India’s DigiYatra (biometric air travel), Singapore’s Smart Water Assessment Network (SWAN), and the UK’s National Grid AI Demand Forecasting System. Data were collected from policy document analysis (2019–2024), semi-structured interviews with 45 cybersecurity and infrastructure governance experts, and publicly available incident reports. A thematic analysis was guided by a conceptual framework integrating institutional trust theory, the NIST AI Risk Management Framework, and GDPR/UK GDPR data protection principles. Cross-case comparison used a most-different systems design to isolate governance effects.

Results reveal three distinct governance-trust configurations. India’s hybrid governance (non-binding guidelines plus sectoral mandates) fosters rapid AI deployment but produces fragmented trust, with high citizen usage alongside low institutional confidence in breach response. Singapore’s centralized, risk-based model (Model AI Governance Framework, amended Cybersecurity Act) generates managed trust—predictable but brittle, with private infrastructure partners exhibiting compliance fatigue. The UK’s principles-based, cross-sectoral approach (e.g., CDEI, NCSC guidance) yields negotiated trust, characterized by active public contestation and slower adoption but higher resilience to adversarial attacks. Cross-cutting findings show that technical robustness alone does not predict trust; instead, transparency mechanisms (e.g., algorithmic impact assessments) and redress pathways are stronger determinants. Notably, all three systems struggle with trust asymmetries: AI operators over-trust automated defenses while citizens under-trust anomaly detection systems.

We conclude that no single governance model universally optimizes cybersecurity trust. India’s agility suits resource-constrained scaling but requires independent oversight for trust repair. Singapore’s precision reduces known risks but may fail against novel AI attacks. The UK’s deliberative model builds legitimacy but at the cost of speed. For policymakers, we recommend (1) embedding ‘trust audits’ as mandatory components of AI system certifications, (2) establishing cross-jurisdictional learning mechanisms for incident response, and (3) moving from static compliance to dynamic trust calibration. Future work should extend the comparison to Global South contexts and empirically test the proposed trust-governance typology.
Keywords Artificial Intelligence (AI) Governance • Cybersecurity Trust • Public Infrastructure Systems • AI Risk Management • Critical Infrastructure Security • AI Policy & Regulation • Trust in AI Systems
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-13
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals