As the adoption of AI models, particularly large language models (LLMs), continues to accelerate, enterprises are growing increasingly concerned about implementing proper security measures to protect these systems. Integrating LLMs into internet-connected applications exposes new attack surfaces that malicious actors could potentially exploit.

Nightfall has been named a Leader in Data Loss Prevention (DLP), Sensitive Data Discovery, and Data Security in G2’s Spring ‘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 secure their sensitive data across SaaS apps, GenAI tools, email, and endpoints.

ICYMI, Nightfall recently launched a suite of enterprise DLP offerings including Data Exfiltration Prevention, Data Encryption, Sensitive Data Protection for SaaS and Email, and SaaS Security Posture Management (SSPM). To celebrate our new offerings, as well as our sixth birthday, we took a moment to gather insights from investors including Ryan Nece, Enrique Salem, Maynard Webb, Frederic Kerrest, and Kelvin Beachum Jr.

Back in 2018, Rohan and I founded Nightfall on the belief that AI could make data leak prevention (DLP) better, faster, and more accessible to every enterprise. At the time, Rohan was a founding engineer at Uber Eats, and I was an investor at Venrock specializing in SaaS and security. From these respective vantage points, we could see that legacy solutions weren’t working, and could never keep up with the evolving threat landscape.

In order to prevent worst-case data leak and data loss scenarios, security teams need to have both proactive and reactive measures in place. Let’s explore this further with an example. Imagine your sensitive data as valuables within a house. There are multiple ways to secure that house, including the following. To put this example into practice, security teams would need to: The first and last of these bullet points fall under the umbrella of SaaS Security Posture Management, or SSPM.

As the adoption of generative AI (GenAI) accelerates across enterprises, one of the most promising applications emerges in customer support. GenAI enables automated responses, allowing businesses to engage in natural conversations with customers and provide real-time chat support. However, this convenience comes with inherent risks, particularly concerning data privacy.

The modern enterprise relies on hundreds of SaaS apps, email services, generative AI (GenAI) tools, custom apps, and LLMs, which often contain sensitive data. For too long, security teams have been forced to patch together point solutions for coverage across these channels, increasing their workloads and creating opportunities for sensitive data to slip through the cracks. This is precisely where Nightfall’s single-pane-of-glass solution comes into play: With Nightfall Sensitive Data Protection.

Did you know that 41% of breaches involve email? For threat actors, cloud email systems like Gmail and Microsoft Exchange are treasure troves for valuable internal information like PII, PCI, PHI, secrets, and credentials. In order to limit the blast radius of privilege escalation attacks, and to remain in compliance with standards like HIPAA, it’s essential for enterprises to protect thousands of emails per day.

Nearly one third of all data breaches are caused by insiders. While you might immediately think of malicious insiders, like disgruntled or departing employees, insider risk can take numerous forms, including: From these examples alone, it’s easy to see just how prevalent insider risk really is. Whether it’s intentional or unintentional, insider risks often have the same consequences as external risks, including data leaks, data loss, noncompliance, and more.