DeepKeep LLM is an end-to-end security and trustworthiness platform for Large Language Models. The solution covers scenarios ranging from agentic AI, through AI applications and MCP, to employees using LLMs in daily work. The platform provides protection against LLM attacks including prompt injection, adversarial manipulation, and semantic attacks. It identifies and alerts against hallucinations using a hierarchical system of data sources with both internal and trusted external references. The solution safeguards against data leakage, protecting sensitive data, personal data, and PII. DeepKeep LLM detects and removes toxic, offensive, harmful, unfair, unethical, or discriminatory language. It ensures ongoing compliance with worldwide AI, cybersecurity, and privacy regulations. The platform addresses multiple security threats including poisoning attacks where hackers change models or datasets, denial of service attacks consuming excessive resources, evasion attacks that mislead LLMs to produce incorrect predictions, and data leakage revealing confidential information. For trustworthiness, the solution addresses fairness issues related to discrimination, toxicity in responses, and hallucinations producing inaccurate or nonsensical outputs. The platform features closed-loop security combining model evaluations and firewall protection, multi-model support including LLM, Vision, and Multimodal models, context-aware security solutions, and native multilingual support.