Protecto, a leader in data security and privacy solutions, is excited to announce its latest capabilities designed to protect sensitive enterprise data, such as PII and PHI, and block toxic content, such as insults and threats within Databricks environments. This enhancement is pivotal for organizations relying on Databricks to develop the next generation of Generative AI (Gen AI)applications.

Hackers recently claimed on a known cybercrime forum that they had stolen hundreds of millions of customer records from Santander Bank and Ticketmaster. It appears that hackers used credentials obtained through malware to target Snowflake accounts without MFA enabled. While it's easy to blame Snowflake for not enforcing MFA, Snowflake has a solid track record and features to protect customer data. However, errors and oversight can happen in any organization.

San Francisco, CA - Protecto, a leading innovator in data privacy and security solutions, is proud to announce the release of new capabilities designed to identify and cleanse Protected Health Information (PHI) data from structured and unstructured datasets, facilitating the creation of safe and compliant data for Generative AI (GenAI) applications. This advancement underscores Protecto's commitment to data security and compliance while empowering organizations to harness the full potential of GenAI.

Retrieval-Augmented Generation (RAG) is a cutting-edge strategy that combines the strengths of retrieval-based and generation-based models. In RAG, the model retrieves relevant documents or information from a vast knowledge base to enhance its response generation capabilities. This hybrid method leverages the power of large language models, like BERT or GPT, to generate coherent and contextually appropriate responses while grounding these responses in concrete, retrieved data.

Large language models (LLMs) have revolutionized various fields by providing advanced natural language processing, understanding, and generation capabilities—these models power applications from virtual assistants and chatbots to automated content creation and translation services. Their proficiency in comprehending and generating human-like text has made them vital resources for businesses and individuals, driving efficiency and innovation across industries.

Large language models (LLMs) have experienced a meteoric rise in recent years, revolutionizing natural language processing (NLP) and various applications within artificial intelligence (AI). These models, such as OpenAI's GPT-4 and Google's BERT, are built on deep learning architectures that can process and generate human-like text with remarkable accuracy and coherence.

Large Language Models (LLMs) have become indispensable tools across various sectors, reshaping how we interact with data and driving innovation in sensitive domains. Their profound impact extends to areas such as healthcare, finance, and legal frameworks, where the handling of sensitive information demands heightened security measures.

Meta Llama 3, the latest iteration of Meta's groundbreaking open-source large language model, marks a significant leap forward in artificial intelligence. Focusing on innovation, scalability, and responsibility, it promises to redefine the landscape of language modeling and foster a thriving ecosystem of AI development.

In a move to harness the transformative power of artificial intelligence (AI) while mitigating associated risks, the Executive Office of the President has issued a landmark memorandum directing federal agencies to advance AI governance, innovation, and risk management. Spearheaded by Shalanda D. Young, the memorandum underscores the importance of responsible AI development in safeguarding the rights and safety of the public.