Authors - Divy Awasthi, Rushil Jariwala, Pearl Patel, Dhiren Patel Abstract - Artificial intelligence is deployed at scale across high-stakes domains—healthcare, autonomous systems, finance, and critical infrastructure— yet the pace of capability development has outrun our ability to ensure these systems behave safely, transparently, and in accordance with human values. While individual aspects of AI safety have been studied in isolation, a unified treatment spanning technical vulnerabilities, ethical risks, security threats, and governance failures remains lacking. This paper addresses that gap with a structured survey of Safe AI organized around a four-layer taxonomy of challenges—data, model, system, and societal—and a corresponding set of mitigation strategies at each layer. We trace AI’s evolution across three generations of increasing capability and opacity, examine domain-specific safety risks in healthcare, autonomous vehicles, manufacturing, and large language models, analyze the alignment problem through robustness, interpretability, controllability, and ethical adherence, and consolidate ten cross-layer directives for safe deployment. We review the global regulatory landscape, including the EU AI Act, GDPR, and national AI safety initiatives across the US, UK, and India, and identify open challenges in scalable oversight, formal verification, and the governance of increasingly autonomous AI systems.
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