Rohan Thakur, Zaman Sajid, Faisal Khan
Artificial intelligence (AI) is rapidly reshaping autonomous systems, enabling faster, more complex decisions with minimal human oversight. This perspective paper argues that a safe and trustworthy AI foundation is essential for the sustainable integration of AI systems into critical domains, supporting responsible and scalable adoption. We propose an independent AI safety system that operates in parallel with operational AI, continuously supervising system safety behavior, detecting anomalies, assessing evolving risks, and supporting conservative interventions when unsafe conditions emerge. This framework emphasizes resilience, accountability, and human-AI interaction by structurally separating performance from system safety through adaptable, transparent, and auditable safety decisions. The proposed architecture represents a conceptual shift by reframing safety from a reactive approach into a proactive domain. This work outlines a pathway for governing AI-enabled autonomy in high-stakes industries by reducing the likelihood of hidden failures and supporting trustworthy operations under uncertainty.