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.

It’s that time of year again: The 2024 Verizon Data Breach Investigations Report is back with the top trends in security breaches over the past year. Read on for an at-a-glance look of some of the report’s most interesting—and actionable—findings.

Cloud storage services and SaaS apps like Google Drive and Microsoft OneDrive provide convenient, scalable solutions for managing documents, photos, and more—making them indispensable for modern work and personal life. However, misconfigured settings and permissions can lead to serious security breaches, noncompliance, and even the loss of customer trust. Let’s explore the 5 most common misconfiguration issues with real-world examples.

From Uber in 2016 to Okta in 2023 to Sisense in 2024, it’s evident that there’s a pattern behind the tech industry’s most devastating breaches: Data sprawl. Let’s dive into how data sprawl played a part in last week’s Sisense breach, as well as how security teams can be proactive in defending against similar attacks.