Database Security

Nightfall AI transforms enterprise DLP with AI-native platform

New enterprise capabilities expand cloud DLP use cases for AI-powered data security

Nightfall AI today unveiled new capabilities to transform data security for the modern enterprise. The industry’s first generative AI (GenAI) DLP platform now offers coverage for SaaS Security Posture Management (SSPM), data encryption, data exfiltration prevention and sensitive data protection. These products expand the company’s existing suite of data leak prevention (DLP) solutions for protecting data at rest and in use across SaaS applications, GenAI tools, email and endpoints.

Data breaches are growing at an alarming rate — 20% in 2023 alone — and costing companies an average of $4.45 million. Legacy data security solutions are failing to keep up; they’re complex and intrusive, and not built for the modern enterprise, where data is shared across hundreds of distributed SaaS applications daily, from email and Slack messages to prompts in GenAI tools like ChatGPT. With new threats emerging constantly, and added regulatory pressure for fast breach disclosure, enterprise security teams need a DLP solution that delivers automation, accuracy and time savings. Nightfall is the first DLP solution to leverage GenAI to prevent data leaks without disrupting modern workflows. It’s AI-native, installs in minutes and provides twice the precision and four times less alert noise than legacy DLP solutions, leading to a strong security posture and lower total cost of ownership.

“Legacy DLP solutions cannot keep pace with the amount of data generated in modern environments. Data exposure and insider risk are often left unchecked, leaving companies and their sensitive data vulnerable,” said Isaac Madan, Nightfall co-founder and CEO. “Nightfall’s industry-first GenAI detectors make the platform a smarter, more scalable way to protect sensitive data in the enterprise. With these new capabilities, companies can secure their data across every data plane, in real time.”

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