When it comes to data loss prevention (DLP) solutions, organizations have several options to choose from. Two prominent players in this space are Nightfall AI and Forcepoint. While both offer valuable features, Nightfall stands out as a superior choice for many businesses. Let's explore why.

Do you have secrets sprawled across your tech stack? The recent Disney breach is as good a reminder as any to check, because it’s likely that you do. But first, let’s take a closer look at the Disney breach to understand what may have gone wrong, and how you can prevent a similar breach from happening to your business.

In today’s cloud-based work environments, it’s all too easy for assets with sensitive data like PII, PCI, PHI, secrets, and intellectual property (IP) to be sprawled across the enterprise tech stack. With the skyrocketing costs of data breaches, one sprawled secret can cost organizations an average of $4.45 million. This is where Data Leak Prevention (DLP) solutions come in to limit secret sprawl, prevent data leaks, and ensure continuous compliance with leading standards.

Whether organizations are looking to prevent data exposure, meet leading compliance standards, or simply earn customer trust, Data Leak Prevention (DLP) policies are effective tools for pinpointing and protecting sensitive data across the cloud and beyond. DLP policies are especially useful in the following top use cases.

Nightfall has been named a Leader in Data Loss Prevention (DLP), Sensitive Data Discovery, Data Security, and Cloud Data Security in G2’s Summer ‘24 reports. We’d like to extend a huge thank you to all of Nightfall’s customers and supporters for making this possible. We’re also happy to acknowledge the Nightfall team’s tireless innovation, all in pursuit of helping customers to protect their sensitive data across the sprawling enterprise attack surface.

Last week’s release of OpenAI’s GPT-4o, along with the story around Slack’s AI training policy, goes to show that AI innovation is happening at a rate where, for most companies, security simply can’t keep up.

AI is everywhere—but how can you be sure that your data isn’t being used to train the AI models that power your favorite SaaS apps like Slack? This topic reached a fever pitch on Hacker News last week, when a flurry of Slack users vented their frustrations about the messaging app’s obtuse privacy policy. The main issue?

After the debut of ChatGPT, and the ensuing popularity of AI, many organizations are leveraging large language models (LLMs) to develop new AI-powered apps. Amidst this exciting wave of innovation, it’s essential for security teams, product managers, and developers to ensure that sensitive data doesn’t make its way into these apps during the model-building phase.

Everywhere we look, organizations are harnessing the power of large language models (LLMs) to develop cutting-edge AI applications like chatbots, virtual assistants, and more. Yet even amidst the fast pace of innovation, it’s crucial for security teams and developers to take a moment to ensure that proper safeguards are in place to protect company and customer data.