Nightfall DLP Platform
AI-native DLP platform for SaaS, GenAI, email, and endpoints with 95% detection accuracy.
Vendor Information
Nightfall DLP Platform Overview
Nightfall is the AI-native data loss prevention platform that protects sensitive data across SaaS applications, GenAI tools, email, and endpoints. The platform features 100+ AI-based models, LLM-powered file classifiers, and computer vision models that achieve 95% detection accuracy—far surpassing legacy DLP solutions stuck at 5-25%. Founded in 2018 and based in San Francisco, Nightfall has raised $60.3M from investors including Bain Capital Ventures, Venrock, and WestBridge Capital. The company serves hundreds of enterprises including Oscar Health, Splunk, and Exabeam.
Nightfall combines real-time content inspection with AI-powered data lineage tracking that traces information from source to destination, understanding risk based on context rather than just pattern matching. The platform deploys in minutes through API-based integrations, lightweight endpoint agents, and browser plugins—providing comprehensive coverage without disrupting productivity. Nightfall Nyx, the industry's first autonomous DLP analyst, automatically investigates security incidents, optimizes policies, and generates reports through natural language interactions, reducing false positives by 90% and cutting investigation time by 80%.
The platform consolidates legacy point solutions into a unified system, enabling customers to replace 3-5 separate security tools while improving protection. With self-learning policies, automated remediation, and end-user coaching for self-remediation, Nightfall delivers enterprise-grade DLP that works from day one. Organizations typically achieve 6x ROI within the first 90 days and complete deployment across their entire environment in under one month.
Key Capabilities
Standardized capabilities mapped to this product's security niche
Discovers and enforces data policies for content stored in or transiting through cloud applications and storage, extending DLP coverage to SaaS environments without endpoint agents.
Extracts text from images, scanned PDFs, and screenshots to classify and detect sensitive data that would bypass text-pattern matching.
Correlates DLP policy violations with user behavioral context, distinguishing routine data movement from anomalous exfiltration patterns associated with insider threat or account compromise.
Applies sensitivity labels to data automatically based on content analysis and context without requiring users to manually classify documents before policy enforcement.
Builds behavioral baselines per user account, device, and application, capturing access timing, resource usage patterns, and activity volumes specific to each entity rather than aggregate thresholds.
Maintains baselines over 30-90 day windows to normalize for seasonal variation, role transitions, and legitimate behavior changes, reducing false positives from periodic patterns.
Models attacker-in-residence scenarios (pre-resignation data staging, after-hours privileged access, bulk download exceeding peer norms), with risk scores decaying appropriately for resolved anomalies.
Integrations
Compatible tools and platforms
Solution Details
Compliance & Certifications
Regulatory frameworks and security certifications
Deployment Options
Where and how this solution can be deployed
Support Channels
Available support and communication options
Pricing Model
How this solution is priced
How to buy
This profile hasn’t been claimed yet. Contact the vendor directly for pricing and purchasing options.
Is this your company?
Claim Your Profile