Legacy data leak prevention (DLP) solutions are failing. Simply put, they weren’t built for business environments rooted in SaaS apps and generative AI (GenAI) tools. Meanwhile, security threats are evolving at a breakneck pace, with as many as 95% of enterprises experiencing multiple breaches a year. New attack surfaces are unfurling at a rapid rate following the switch to hybrid and cloud-based workspaces.

This month, the Nightfall team has some exciting product enhancements to share with you, including an expansion of our secrets detectors as well as brand new APIs for automated workflows. Dive in to find out what we've been working on—as well as what's coming next.

Since the widespread migration to the cloud, DLP has become an essential—yet often dreaded—tool for protecting data from leaks, breaches, exfiltration, and more. It’s no secret that traditional DLP solutions have a less-than-stellar reputation. Security teams are squeezed tighter than ever in terms of time and resources. Needless to say, adding more alerts on top of already daunting workloads is less than ideal. It’s time for a smarter, more sustainable form of DLP.

Nightfall is starting off the new year strong with several exciting updates, including a smoother Google Drive remediation workflow, an automatic annotations feature, and all-new policy pages for Atlassian and Google Drive. Read on to learn more about what we’ve been up to this month.

As we kick off the new year, we're excited to look back on all that we learned in 2023. This past year saw some momentous advancements, including the large-scale adoption of generative AI (GenAI). However, it also saw some devastating data breaches. According to IBM’s latest “Cost of a Data Breach” report, 95% of studied companies experienced a breach in 2023.

Looking for ways to simplify your cloud DLP workflows in 2024? Read on for 3 ways that Tines—our go-to secure workflow builder—can make your resolutions a reality. First, let’s learn a little about how Tines works. In short, Tines helps users to create “stories” (aka workflows) that streamline communications, automate tasks, and more. Tines stories can take any number of twists and turns by: But how can you put these actions into practice?

How many of us use ChatGPT? And how many of us use SaaS applications as part of our daily workflows? Whether you know it or not, if you use either of these tools, your data has likely traveled beyond the boundaries of your “fort.” What do I mean by “fort,” exactly? For this guide, consider your “fort” to be somewhere where you can monitor and secure your data. When data leaks outside your “fort,” it presents a myriad of possible risks.

It’s official: Nightfall has been named as a leader in Data Loss Prevention (DLP), data security, and sensitive data discovery by G2’s peer-reviewed marketplace. At Nightfall, our goal is to help customers to discover, classify, and protect their sensitive data across the cloud.

There are various ways to measure any given machine learning (ML) model’s ability to produce correct predictions, depending on the task that the system performs. Named Entity Recognition (NER) is one such task, in which a model identifies spans of sensitive data within a document. Nightfall uses NER models extensively to detect sensitive data across cloud apps like Slack, Microsoft Teams, GitHub, Jira, ChatGPT, and more.

Nightfall AI is excited to announce a new generation of detectors powered by generative AI (GenAI). Read on to learn more about recent advancements in our PII, PHI, secrets, and images detectors—as well as how they stack up against competitors like AWS Comprehend, Google DLP, and Microsoft Purview.