Nightfall AI

Modern DLP uses artificial intelligence to identify sensitive data like Personally Identifiable Information (PII), Payment Card Information (PCI), Protected Health Information (PHI), credentials (passwords, API keys), and other confidential information. AI is crucial because it can understand context and variations in data formats much better than older rule-based systems, leading to fewer false positives and more accurate detection.

This focuses on stopping sensitive data from leaving the organization's network or control. This can involve blocking transfers, encrypting data in transit, or applying other controls to prevent unauthorized access and sharing.

"Secrets" like API keys, passwords, and other credentials often get scattered across various systems (code repositories, chat apps, documents). This sprawl creates significant security risks. DLP solutions help identify and control these secrets, reducing the chances of unauthorized access.

When sensitive data is detected in violation of policy, automated remediation takes action. This might involve quarantining the data, notifying the user, or even automatically encrypting the data. Automation is essential for fast and efficient incident response.

Before you can protect data, you need to know where it is and what type of data it is. Data discovery tools scan various systems to locate sensitive data. Classification categorizes the data based on its sensitivity level (e.g., confidential, public).

Encryption makes data unreadable to unauthorized parties. DLP solutions often integrate with encryption tools to protect sensitive data both in transit and at rest.

This is a broader category that encompasses DLP but also includes other measures to safeguard data.

Specifically encrypting data when it's being sent outside the organization (e.g., in emails or file transfers).

Ensuring that sensitive data is stored securely, with appropriate access controls and encryption.

This is a newer area of DLP, addressing the unique challenges of protecting data used in Artificial Intelligence.

This type of firewall controls what data AI developers can use when building and training AI models. It prevents sensitive data from being incorporated into models where it could be exposed or misused.

Controls the data that AI applications (like chatbots or AI assistants) can access. This prevents these applications from inadvertently revealing sensitive information.

These are core capabilities of the DLP platform itself.

This emphasizes empowering users to be part of the data protection process. It might involve educating users about data security policies and giving them tools to identify and report potential data leaks.

The core detection engine uses AI as its primary method for identifying sensitive data.

Comprehensive coverage across all the places where data lives and is used: Software as a Service (SaaS) applications, user devices (endpoints), and within AI systems.

The DLP platform is designed to run in cloud environments, offering scalability and flexibility.

The platform can handle large volumes of data and operate efficiently without impacting performance.

These are ways the DLP platform is applied to solve specific problems.

Pre-built configurations and workflows designed for common DLP scenarios (e.g., protecting PII, preventing financial data leaks).

Solutions tailored to the specific regulatory and compliance requirements of different industries.

Features and tools that help organizations meet specific compliance standards.

How the DLP platform connects with other systems.

Direct connections to popular SaaS apps to monitor and control data within those applications.

Connections to AI platforms to secure data used by those tools.

Tools to monitor and control data on user devices and within web browsers.

Features for developers to customize and extend the DLP platform.

(As described above in Data Privacy for AI)

Allowing developers to connect the DLP platform to their own systems and workflows.

Tools and code samples for developers to use when integrating with the platform.

A sandbox environment where developers can test the detection engine and experiment with different configurations.