Data Leakage Prevention in LLMs
(Gen AI)

We offer solutions to detect and prevent data leakage within LLMs (Gen AI), utilizing advanced techniques to monitor and secure data handling throughout AI operations.

We focus on maintaining data privacy while maximizing the LLM’s utility, crucial for handling sensitive information.

Key Benefits

Enhanced Model Reliability
Ensures AI models retain their accuracy and effectiveness, reducing the risks associated with outdated or degraded model performance.

Structured Compliance
Facilitates adherence to both internal and external governance requirements, adding accountability throughout the AI model lifecycle.

Greater Transparency and Control
Increases visibility into AI model operations, supporting better decision-making and risk management.

Hidden Risks

Private training data can be exposed through gradient-sharing mechanisms in federated learning systems, even in large-batch settings, underscoring critical data leakage risks in AI models.

Source: arXiv, 2024.

Seamless Integration

Integrates with existing data loss prevention (DLP) tools and policies, providing a seamless layer of security for data processed by LLMs.

Can be implemented alongside access control and encryption systems, enhancing the overall security framework and protecting data at all stages of the LLM workflow.

Works well with data auditing and monitoring tools, enabling real-time detection and response to potential data leaks within AI systems.

Machine learning models can unintentionally leak sensitive data from their training sets, either through the models themselves or their predictions. This highlights the urgent need for robust methods to quantify and mitigate information leakage.

Source: arXiv, 2024.

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Data Leakage Prevention in LLMs(Gen AI)