Large Language Models (LLMs) have advanced significantly in text generation and integration with external applications, but they also present security challenges that require careful management. The potential misuse by malicious actors raises concerns about social engineering, data exfiltration, and other security risks, prompting the need for comprehensive protective measures. LLM security involves safeguarding data, models, and infrastructure from unauthorized access and bias, with strategies ranging from fine-tuning models to implement ethical guidelines, to employing external censorship mechanisms. The implementation of security practices, such as regular audits and incident response planning, is critical for ensuring the reliability and trustworthiness of LLM outputs. Tools like Lakera Guard offer model-agnostic security enhancements for LLM applications, and organizations are encouraged to adopt best practices and comply with emerging regulations like the EU AI Act. The significance of governance, legal frameworks, and real-world insights, as well as resources like the AI Incident Database, are emphasized for a holistic approach to navigating the evolving landscape of LLM security.