Cato Networks DLP vs Nightfall Data Loss Prevention: Side-by-Side Comparison (2026)

Cato Networks DLP: Cloud-native DLP for protecting sensitive data across users, locations, and clouds. built by Cato Networks. Core capabilities include 350+ pre-defined data types catalog, Custom data type creation with keywords and regular expressions, Microsoft Information Protection (MIP) label support..

Nightfall Data Loss Prevention: AI-powered DLP platform for SaaS, AI apps, and endpoints. built by Nightfall AI. Core capabilities include Data exfiltration prevention across endpoints, browsers, email, and removable media, Real-time detection and blocking of sensitive data in SaaS and AI applications, Data discovery and classification for sensitive data at rest..

Both serve the Data Loss Prevention market but differ in approach, feature depth, and target audience.

Cato Networks DLP differentiates with 350+ pre-defined data types catalog, Custom data type creation with keywords and regular expressions, Microsoft Information Protection (MIP) label support. Nightfall Data Loss Prevention differentiates with Data exfiltration prevention across endpoints, browsers, email, and removable media, Real-time detection and blocking of sensitive data in SaaS and AI applications, Data discovery and classification for sensitive data at rest.

Cato Networks DLP is developed by Cato Networks. Nightfall Data Loss Prevention is developed by Nightfall AI. Vendor maturity, funding stage, and team size can be important factors when evaluating long-term viability and support quality.

Cato Networks DLP integrates with Microsoft Information Protection, ChatGPT. Nightfall Data Loss Prevention integrates with Slack, Microsoft 365, Google Workspace, GitHub, Salesforce and 3 more. Check integration compatibility with your existing security stack before deciding.

Cato Networks DLP and Nightfall Data Loss Prevention serve similar Data Loss Prevention use cases: both are Data Loss Prevention tools. Review the feature comparison above to determine which fits your requirements.