Nightfall is the AI-native DLP platform that prevents sensitive data exposure and exfiltration across SaaS, endpoints, email, browsers, and AI apps. At our core are 100+ AI-based models, LLM based file classifiers and Computer Vision models that classify content with 95% accuracy - far surpassing legacy solutions stuck at 5-25%. We combine content inspection with AI-based data lineage tracking that traces information from source to destination, understanding risk based on context, not just content patterns. API-based SaaS integrations deploy in minutes, while lightweight endpoint agents and browser plugins provide comprehensive coverage without disrupting productivity. Our platform learns continuously, automatically improving detection and reducing false positives over time.

Nightfall eliminates the three critical failures plaguing legacy DLP: detection blind spots, operational overload, and deployment friction. While legacy solutions rely on basic pattern matching with 5-25% accuracy and require months of tuning, Nightfall's AI-based detectors, LLM based file classifiers and Computer Vision models deliver 95% accuracy out-of-the-box. Our AI-based data lineage traces data from source to destination, determining risk regardless of content transformation - catching sophisticated exfiltration risks that bypass traditional regex rules. You can deploy Nightfall in hours via API integrations across SaaS apps like Microsoft 365, Google Workspace, Atlassian, Salesforce, Slack, Zendesk, Notion, endpoint agents on macOS and Windows, browser plugins, and any AI app, with zero productivity impact and no single points of failure.

Nightfall combines AI-powered content classification with intelligent data lineage to stop data loss before it happens. Our platform continuously monitors data movement across SaaS apps, endpoints, email, browsers, and Shadow AI apps, classifying sensitive content including secrets & credentials, PHI, PCI, and PII with 95% precision. Additionally, Nightfall also supports LLM powered file classifiers - pre-trained models that identify sensitive document types based on structure, layout, and semantic meaning, not just keywords or entity matches. These file classifiers classify data across categories such as Financial Statements & Revenue Reports, Internal Source Code & Engineering Artifacts, HR Records & Personnel Files, Contracts, NDAs, and Legal Agreements, Product Roadmaps & R&D Specifications, Tax Filings, Audit Docs, and Compliance Records. When risky behavior is detected, Nightfall automatically enforces context-aware policies: blocking high-risk exfiltration to unauthorized destinations, redacting sensitive information in SaaS apps, revoking inappropriate access permissions, blocking, deleting, quarantining or encrypting risky data - all while maintaining visibility and allowing legitimate business workflows to proceed unimpeded.

Nightfall provides comprehensive Shadow AI security across any generative AI app including ChatGPT, Copilot, Gemini, Deepseek, Claude, Perplexity and more. Our browser plugins and endpoint agents monitor AI interactions in real-time, intercepting sensitive data before it reaches AI platforms. Nightfall automatically blocks secrets, credentials, PHI, PCI, or other confidential information via file uploads or clipboard copy/paste actions. Unlike solutions that discover Shadow AI usage after the fact, Nightfall prevents data leaks at the moment of interaction, enabling your teams to innovate safely with AI while maintaining complete control over intellectual property and regulated data.

Nightfall detects malicious insiders deliberately stealing data, opportunistic insiders collecting sensitive information over time for later misuse, and negligent insiders accidentally exposing data through policy violations. Our AI-powered platform identifies unusual download patterns, unauthorized data movement to personal devices or domains, suspicious file exfiltration over days or weeks, and sophisticated exfiltration attempts across multiple channels simultaneously. Data lineage tracking provides complete visibility into the origin, journey, and destination of sensitive information, enabling accurate detection of threats that unfold gradually. LLM-powered risk scoring distinguishes between routine business activity and true threats, dramatically reducing false positives while ensuring real insider risks don't slip through.