Year: 2026
Type: article
Abstract: As artificial intelligence (AI) systems become increasingly autonomous, scalable, and embedded in critical digital infrastructure, AI safety has emerged as a significant consideration for cybersecurity, system reliability, and institutional trust. Advances in large language models and agentic systems expand the threat surface to include misalignment, large-scale misuse, opaque decision-making, and cross-border risk propagation, while existing debates remain fragmented across technical, ethical, and geopolitical domains. This paper conducts a structured comparative analysis of AI safety perspectives from ten influential thinkers, examining them across five dimensions and reframing their insights through a cybersecurity lens spanning national governance, industry standards, and firm-level design. Building on this synthesis, the study proposes a layered control architecture that organizes technical safeguards, governance mechanisms, and human oversight into a defense-in-depth structure. The framework is conceptual and theory-building, intended to clarify system-level security reasoning and support future empirical refinement across diverse institutional contexts.
Source: Systems
Authors: Young B. Choi, Paul C. Hong, Young Soo Park
Institutions: Regent University, University of Toledo, Midwest University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7155001936