Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Nightfall

Nightfall AI: AI-Powered Data Leak Prevention (DLP) for the Enterprise

Data leak prevention (DLP) has become a critical tool for securing the modern enterprise. Think of popular workplace apps like Slack, Salesforce, Google Drive, M365, ChatGPT, and more; these apps have revolutionized workplace productivity, but they’ve also provided new pathways to spread sensitive data and risk compliance. This is where DLP solutions come in. However, legacy DLP relies on rules and heuristics, which overload security teams with false positive alerts and slow the remediation process to a grinding halt.

Nightfall Sensitive Data Protection for Email

Leverage Nightfall’s AI-native platform to pinpoint and protect PII, PCI, PHI, secrets, and credentials across SaaS and email, including Gmail. Built with AI at the core, Nightfall Sensitive Data Protection is transforming email DLP by helping security teams to… … detect sensitive data with 2x better precision and 4x fewer false positive alerts. … act swiftly by blocking or quarantining emails, or removing attachments that contain sensitive data.

Nightfall Data Exfiltration Prevention

Nightfall Data Exfiltration Prevention uses generative AI to discover sensitive data and monitor data movement across SaaS apps like Google Drive. Nightfall’s enterprise-grade data leak prevention platform offers several key benefits, such as… … complete coverage across SaaS apps and managed endpoints. … enhanced detection accuracy, leading to 4x fewer false positive alerts. … streamlined workflows, so security teams can monitor data movement and take action from within a single user-friendly console.

Nightfall SaaS Security Posture Management (SSPM)

Nightfall SaaS Security Posture Management (SSPM) offers real-time visibility into permissions and sharing settings to prevent security posture drift. Nightfall leverages generative AI (GenAI) for a dynamic and responsive management approach to SaaS security posture, which means… … unparalleled visibility into sharing and permissions settings across SaaS apps. … enhanced detection accuracy, leading to 4x fewer false positive alerts.

Here's what caused the Sisense data breach-and 5 tips for preventing it

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.

Nightfall AI: The First AI-Native Enterprise DLP Platform

Legacy DLP solutions never worked. They're point solutions that generate an overwhelming number of false positive alerts, and block the business in the process. But no longer. Enter: Nightfall AI, the first AI-native enterprise DLP platform that protects sensitive data across SaaS, generative AI (GenAI), email, and endpoints, all from the convenience of a unified console.

Nightfall named a "Data Security Solution of the Year"

We’re thrilled to announce that Nightfall was selected as the “Data Security Solution of the Year” in the 2024 Data Breakthrough Awards. With enterprises scrambling to stay on the cutting edge of innovation, it’s all too easy to lose sight of data stewardship. In addition to SaaS apps, email, and endpoints, now enterprises must also safeguard their generative AI (GenAI) applications, including both custom and third-party GenAI tools.

Securing AI with Least Privilege

In the rapidly evolving AI landscape, the principle of least privilege is a crucial security and compliance consideration. Least privilege dictates that any entity—user or system—should have only the minimum level of access permissions necessary to perform its intended functions. This principle is especially vital when it comes to AI models, as it applies to both the training and inference phases.

Firewalls for AI: The Essential Guide

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.