Nightfall AI

Additional features

AI-powered sensitive data detection (PII, PCI, PHI, credentials, etc.)

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.

Data exfiltration prevention

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 sprawl prevention

"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.

Automated remediation

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.

Data discovery and classification

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).

Data encryption

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

Data Protection

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

Data encryption for outbound communications

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

Secure storage of sensitive data

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

Data Privacy for AI

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

Firewall for AI developers (preventing sensitive data use in models)

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.

Firewall for AI copilots (controlling data access for AI applications)

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

Platform Features

These are core capabilities of the DLP platform itself.

Human firewall (user-centric approach to data protection)

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.

AI-native detection engine

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

360° protection across SaaS, endpoints, and AI

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.

Cloud-native architecture

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

Scalability and performance

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

Solutions

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

Use case-specific solutions for different data protection needs

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

Industry-specific solutions (SaaS, healthcare, financial services)

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

Compliance support (HIPAA, PCI, GDPR/CCPA, ISO 27001, SOC 2, SOX)

Features and tools that help organizations meet specific compliance standards.

Integrations

How the DLP platform connects with other systems.

Extensive integrations with SaaS applications (Slack, Jira, Salesforce, etc.)

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

Integrations with GenAI tools (ChatGPT)

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

Integrations with endpoints and browsers

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

Developer Tools

Features for developers to customize and extend the DLP platform.

Firewall for AI

(As described above in Data Privacy for AI)

API access for custom integrations

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

Libraries and SDKs

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

Detection playground

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