Secure AI Summit 2024 (Powered by Cloud Native)

Cato Networks has recently released a new data loss prevention (DLP) capability, enabling customers to detect and block documents being transferred over the network, based on sensitive categories, such as tax forms, financial transactions, patent filings, medical records, job applications, and more. Many modern DLP solutions rely heavily on pattern-based matching to detect sensitive information. However, they don't enable full control over sensitive data loss. Take for example a resume of a job applicant. While the document might contain basic PIIs, such as the candidate's phone number and address, it's the application itself that concerns the company's DLP policy. Unfortunately, pattern-based methods fall short when trying to detect the document category. Many sensitive documents don't have specific keywords or patterns that distinguish them from others, and therefore, require full-text analysis. In this case, the best approach is to apply data-driven methods and tools from the domain of natural language processing (NLP), specifically, large language models (LLM).