Monitoring and auditing are critical components of cybersecurity, designed to detect and prevent malicious activities. Monitoring involves real-time observation of system activities, while auditing entails a systematic review of logs and interactions. Large Language Models (LLMs), such as GPT-4, are increasingly integrated into various applications, making them attractive targets for cyber threats.

The National Institute of Standards and Technology (NIST) has announced the launch of Assessing Risks and Impacts of AI (ARIA), a groundbreaking evaluation program to guarantee the secure and trustworthy deployment of artificial intelligence. Spearheaded by Reva Schwartz, ARIA is designed to integrate human interaction into AI evaluation, covering three crucial levels: model testing, red-teaming, and field testing.

API Management is a comprehensive process that involves creating, publishing, documenting, and overseeing application programming interfaces (APIs) in a secure, scalable environment. APIs are the backbone of modern software architecture, enabling interoperability and seamless functionality across diverse applications. They facilitate the integration of different software components, allowing them to intercommunicate and share data efficiently.

The rapid rise of low-code and no-code platforms has democratized application development, enabling even non-technical business users to swiftly create critical business applications. However, this accessibility brings new security challenges, particularly with the integration of AI technologies such as copilots, which are used to automate tasks and enhance functionality within these platforms. Zenity enhances the security of these AI-integrated environments by managing and securing AI copilots.

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.