Large language models (LLMs) have garnered widespread attention due to their impressive performance across a range of applications. However, our understanding of the trustworthiness and risks of these models remains limited. The temptation to deploy proficient foundation models in sensitive domains like healthcare and finance, where errors carry significant consequences, underscores the need for rigorous safety evaluations, enhancement, and guarantees. Recognizing the urgent need for developing safe and beneficial AI, our recent research seeks to design a unified platform to evaluate the safety of LLMs from diverse perspectives such as toxicity, stereotype bias, adversarial robustness, OOD robustness, ethics, privacy, and fairness; enhance LLM safety through knowledge integration; and provide safety guardrail and certifications. In this talk, I will first outline our foundational principles for safety evaluation, detail our red teaming tactics, and share insights gleaned from applying our DecodingTrust platform to different models, such as proprietary and open-source models, as well as compressed models. Further, I will delve into our methods for enhancing model safety, such as hallucination mitigation. I will also explain how knowledge integration helps align models and prove that the RAG framework achieves provably lower conformal generation risks compared to vanilla LLMs. Finally, I will briefly discuss our robust guardrail framework for risk mitigation in practice.