LLM Access Shield: Preventing Data Leakage & Undesirable Responses

As LLMs are adopted across finance, legal, and education, risks of data leakage and undesirable responses grow. Existing LLMs lack precise, organization-specific sensitive data detection, cannot anonymize data while preserving contextual semantics, and do not allow for dynamic policy customization, resulting in security, compliance, and trust challenges.

• User-configurable sensitive data taxonomy and policy management enable cross-industry, cross-department security needs, with dynamic adjustment of detection rules.
• Domain-specific LLM for Security (DLMS) integrates parameter-efficient fine-tuning (LoRA) for accurate detection and classification of multiple sensitive entities.
• Format-preserving encryption (FPE) anonymizes sensitive data without altering structure or contextual semantics, balancing data utility and privacy compliance.

• Dramatically reduces risks of sensitive data leaks and unsafe LLM outputs, enhancing organizational data security and compliance.
• Dynamic strategies and customizable policies respond instantly to evolving regulations and industry needs, supporting scalable deployment.
• Modular architecture integrates easily with existing IT systems and can be tailored for different industries and use cases.
• Administrator interface and dashboard support real-time monitoring, alerting, and comprehensive compliance reporting, improving operational transparency.

• Sensitive data protection in enterprise LLM usage
• AI compliance solutions for finance, healthcare, and legal sectors
• Data privacy protection, anonymization, and automated audit workflows
• Secure monitoring and policy enforcement for AI responses

• US Application. No. 19/189,211; CN Patent application in process