In the age of generative AI, data security is a key concern for organizations to manage. In my previous blog post, I dug into how modern SSE technology helps to better secure genAI. The recently published ebook Securing GenAI for Dummies offers further clarity on strategies organizations can use when it comes to securing and enabling genAI apps. With that in mind, we’ve compiled 10 essential questions to keep in mind as you assess your data security, along with how Netskope can help address them.

If you’ve been paying attention to the latest AI product releases or evaluating AI tools for your teams, you’ll probably have noticed how difficult it is to distinguish between hype and reality. Vendors are under an enormous amount of pressure to deliver AI features, and, as a result, many of these new tools feel rushed and fragile, and simply aren’t capable of solving important, real-world problems.

Developers often use two prominent techniques for enhancing the performance of large language models (LLMs) are Retrieval Augmented Generation (RAG) and fine-tuning. Understanding when to use one over the other is crucial for maximizing efficiency and effectiveness in various applications. This blog explores the circumstances under which each method shines and highlights one key advantage of each approach.

When evaluating models or products for their ability to scan and mask Personally Identifiable Information (PII) in your data, it's crucial to follow a systematic approach. Let’s assume you have a dataset with 1,000,000 rows, and you want to scan and mask each row.

In a world where cybersecurity threats are ever evolving and increasingly sophisticated, businesses of all sizes need robust solutions to protect their networks. However, these solutions have traditionally been costly and complex. WatchGuard is changing that tradition with the launch of its new ThreatSync+ Network Detection and Response (NDR) solution.

Brazil's data protection authority, Autoridade Nacional de Proteção de Dados (ANPD), has issued a temporary ban on Meta from processing personal data of users to train its artificial intelligence (AI) algorithms. This decision stems from concerns over inadequate legal justification, lack of transparency, and potential risks to privacy rights, particularly for children and adolescents.

In the evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as pivotal technology, driving advancements in natural language processing and generation. LLMs are critical in various applications, including chatbots, translation services, and content creation. One powerful application of LLMs is in Retrieval-Augmented Generation (RAG), where the model retrieves relevant documents before generating responses.

For years, we as security practitioners were promised that AI and machine learning would change our lives for the better, but time and time again, the companies that touted this technology disappointed us. In the first decade or so of AI-branded security tools, we saw plenty of products that demoed well, but were completely undeployable.

The number 42 has gained legendary status in popular culture, largely due to Douglas Adams’ science fiction series, The Hitchhiker’s Guide to the Galaxy. In this series, a group of hyper-intelligent beings build a supercomputer named Deep Thought to calculate the “Answer to the Ultimate Question of Life, the Universe, and Everything”. After much contemplation, Deep Thought reveals the answer to be simply “42”.