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.
Choi et al. (2026) studied this question.