DeepKeep’s solution for LLM
Security and trustworthiness are critical not only when using an external LLM (like ChatGPT, Gemini, Copilot and others), but even more so when using internal fine-tuned LLM.
Our end-to-end solution covers scenarios ranging from agentic AI, through AI applications and MCP, and all the way to employees making daily use of LLMs as part of their work:
Protects against LLM attacks, including prompt injection, adversarial manipulation and semantic attacks
Identifies and alerts against hallucination using a hierarchical system of data sources, including both internal and trusted external references
Safeguards against data leakage, protecting sensitive data and personal data or PII
Detects and removes toxic, offensive, harmful, unfair, unethical, or discriminatory language
Ensures on-going compliance with worldwide AI, cybersecurity and privacy regulations
Security
Securing AI models from the development phase of machine learning models through the entire product lifecycle, including risk assessment, protection, monitoring and mitigation.
Trustworthiness
Valid, reliable, resilient, accountable, fair, transparent and explainable.
The DeepKeep factor
Closed-Loop Security and Trustworthiness
End-to-end platform providing model evaluations and firewall protection, creating a closed loop to identify and resolve vulnerabilities seamlessly.
Multi Model Support
Supporting diverse models, including LLM, Vision, and Multimodal, the only platform addressing physical security.
Context
Delivering security solutions with context awareness, ensuring more realistic, adaptive, and effective protection against evolving threats.
Multilingual Support
Providing native support for multiple languages.