Safeguarding Data Privacy For Trusted AI 

The rapid adoption of traditional and generative AI has triggered a surge in the demand for data, but managing data at scale has created numerous challenges for enterprises in implementing AI safely, efficiently, and effectively. Key challenges include preventing the leakage of PII in models, precise classification and de-identification of sensitive data and PII in data lakes for safe use in analytics, model training and other downstream business processing.

Join us for an engaging discussion to:

• Understand the limitations and shortcomings of existing cloud DLP tools and “homegrown” techniques for petabyte-scale data. 
• Discover how Granica unlocks up to 5-10x more data for its safe use in training pipelines with best-in-class accuracy and performance. 
• Achieve improved PII de-identification via data type specific detection methods
• Learn how Granica can be leveraged as part of a secure trust layer for large language models to effectively curb the leakage of PII.