The Comprehensive LLM Safety Guide: Navigate AI regulations and Best Practices for LLM Safety

Large Language Models (LLMs) are becoming increasingly powerful and autonomous, leading to a greater need for ensuring their safety. This involves addressing vulnerabilities such as data protection, content moderation, and reducing harmful or biased outputs in real-world applications. Governments worldwide are stepping up with new AI regulations, and extensive research is underway to develop risk mitigation strategies and frameworks. LLM Safety focuses on safeguarding these models, ensuring they function responsibly and securely. Key areas of concern include responsible AI risks, illegal activities risks, brand image risks, data privacy risks, and unauthorized access risks. Various benchmarks and evaluation tools are available to assess LLMs for vulnerabilities, while mitigation frameworks like Anthropic's ASL, Google DeepMind's Frontier Safety Framework, Meta's Llama Guard, and OpenAI's Moderation API provide strategies for addressing these risks. Challenges in maintaining LLM safety include limited tools for transparency, human-in-the-loop constraints, continuous feedback and adaptation gaps, environment-specific solutions, exclusive moderation ecosystems, and the absence of centralized risk management. Confident AI offers comprehensive vulnerability and production monitoring across use cases to address these issues.