Harnessing the benefits of
LLM without the risks
DeepKeep enables enterprises to leverage the benefits of LLM, in a secure, risk-free way
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.
Poisoning
When hackers purposely change a model or dataset.
Denial of Service
When attackers consume excessive resources while interacting with an LLM.
Evasion
When attackers mislead an LLM to produce incorrect or biased predictions.
Data leakage
When an LLM reveals confidential, sensitive or proprietary information through its responses.
PII Privacy
When a model jeopardizes personal identifiable information, such as digits that are part of a social security number, or bank account.
Trustworthiness
Valid, reliable, resilient, accountable, fair, transparent and explainable.
Fairness
When an LLM discriminates based on gender, age, ethnicity, or anything else.
Toxicity
When an LLM is rude, disrespectful, or makes an unreasonable comment. F*cking unbelievable!
Hallucination
When a model’s response contains inaccurate information, producing outputs that are nonsensical, misleading, or irrelevant.
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.
LLMs lie and make mistakes. The question is not if, but when and how.
Security Breach
Banks are already using AI models to streamline decision-making processes about a wide rangeof topics, such as credit and algorithmic trading. However, AI models are subject to anomalousand malicious inputs, as well as unexpected behavior.
Exploiting Data Leakage
An insurance company employs LLM-based customer support by utilizing a walledgarden application. This application has been trained to steer away from any questions notpertaining to predetermined topics. However, attackers are exploiting customer supportinterfaces, breaching LLM guardrails to extract data that goes beyond the intended scope ofassistance.
