Browser-native interception blocks real-time data exfiltration to AI tools, encrypted web apps, and unmanaged cloud services
Nightfall announced the launch of its AI Browser Security solution, designed to stop real-time data exfiltration through AI tools, AI-powered browsers, and modern web workflows that legacy data loss prevention (DLP) solutions cannot see or control.
As employees increasingly rely on ChatGPT, Claude, Gemini, Copilot, and emerging AI-native browsers to analyze documents, debug code, and summarize business data, sensitive information is routinely exposed through browser-based uploads, clipboard pastes, screenshots, and autonomous agent interactions. Traditional DLP tools—built for email attachments, USB drives, and static pattern matching—lack visibility inside browsers and encrypted sessions, leaving organizations blind to their fastest-growing data loss vector.
Nightfall closes this gap with an AI-native security architecture that operates directly at the browser, endpoint, and SaaS layers—where modern exfiltration actually happens—enabling real-time prevention before sensitive data ever leaves the organization.
“AI browsers are fast becoming the primary data exfiltration channel inside the enterprise,” said Rohan Sathe, CEO and co-founder of Nightfall. “Employees aren’t bypassing security out of malice; they’re pasting code, uploading spreadsheets, and sharing screenshots to get work done. Legacy DLP was never designed to see or understand those actions. Nightfall’s AI-native browser security gives teams visibility and control at the exact moment data is shared.”
Shadow AI Has Outpaced Legacy Security
Legacy security architectures were designed for a pre-AI world, where sensitive data moved through email, file transfers, and known SaaS applications. Today’s workflows look very different:
- Proprietary source code is pasted directly into AI chat interfaces
- Financial and customer data is dragged into AI tools over encrypted HTTPS
- Screenshots and images bypass file-based controls entirely
- Data lineage is lost as content moves between SaaS apps, endpoints, and browsers
Because traditional DLP relies on regex rules, network inspection, and after-the-fact alerts, these workflows often go undetected—until sensitive data has already left the organization.
Nightfall’s AI-native approach was purpose-built to address these blind spots.
How Nightfall Protects AI-Native Workflows
Nightfall’s AI Browser Security solution delivers comprehensive coverage across every major data exfiltration path:
- Browser-Native Interception: Nightfall operates directly inside modern browsers—including Chrome, Edge, Firefox, Safari, and AI-based browsers such as Comet, Atlas, and other Chromium browsers such as Arc and Brave—providing real-time visibility into file uploads, clipboard paste actions, form submissions, and screenshot-based sharing to any website or AI application. Content is analyzed and blocked before transmission, without proxies, SSL inspection, or workflow disruption.
- Comprehensive Endpoint Coverage: Endpoint agents extend protection beyond the browser, monitoring cloud sync tools, desktop AI applications, Git and CLI operations, USB transfers, printing, and clipboard activity across applications—closing common workarounds used to bypass traditional DLP.
- SaaS API Enforcement and Data Lineage: Native integrations with platforms such as Google Drive, Microsoft 365, Slack, Salesforce, GitHub, and Zendesk enable continuous scanning of data at rest and in motion, with full visibility into where sensitive data originates, how it is transformed, and where it is headed.
AI-Powered Detection Built for Context, Not Patterns
All enforcement layers are powered by Nightfall’s AI-native detection engine:
- Machine learning models deliver high-precision detection for credentials, PII, PCI, and PHI without manual tuning
- LLM-powered document classification understands business context to identify source code, customer lists, financial projections, board materials, and proprietary intellectual property
- Computer vision and OCR detect sensitive information embedded in screenshots, scanned documents, and images before they are shared
Unified data lineage traces content from source to attempted destination, providing forensic-grade visibility for security teams
Unlike legacy DLP, Nightfall’s detections are explainable, adaptive, and enforced in real time—preventing exposure rather than alerting after the fact.
Nightfall applies a single, unified policy framework across all layers of enforcement. Security teams can define rules such as blocking financial data from being uploaded to external AI tools or preventing proprietary source code from leaving development environments—without managing disconnected tools or inconsistent controls.
This unified approach enables organizations to safely enable AI adoption while maintaining the visibility, governance, and control required in regulated and high-risk environments.
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