Nightfall shines light on AI browser data exfiltration

Wake up at Nightfall

San Francisco–based Nightfall positions itself as a specialist in this space with a mission and set of core platform competencies designed to provide AI-native data loss prevention to protect sensitive data across SaaS applications, generative AI tools, browsers, email and all forms of computing endpoints. 

The company has this month announced its AI Browser Security service, a technology layer 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.

So, is this really a thing? Very much so, allow us to clarify, validate and explain.

How do AI-powered browsers work?

Very much so… real-time data exfiltration happens through AI-powered browsers due to the browser operating as what we can call an “active participant” (rather than working as a passive viewer) and although users can think of a traditional browser as an application for website viewing and not a whole lot more, an AI browser often comes with integrated sidebars and assistant functions which underpin the permission it has to read and scrape a users screen, keep a record of the user’s website history and then perform actions and act on the user’s behalf.

Because enterprise employees increasingly rely on ChatGPT, Claude, Gemini, Copilot and other emerging AI-native browsers to analyse documents, debug code and summarise business data, sensitive information is routinely exposed through browser-based uploads, clipboard pastes, screenshots and autonomous agent interactions. 

A closely related “phenomenon” here is sidebar spoofing; this occurs when malicious actors compromise browser extensions to make them look, appear and act just like the abovementioned AI tools and copilots. Every keystroke the user makes and every login credential or other item of interaction is dispatched to the attacker’s server; the malicious actor can even use AI services to reply to the users so that the experience feels authentic, all while real-time data exfiltration is happening.

The situation is compounded by the fact that traditional DLP tools – built for email attachments, USB drives and static pattern matching – typically lack visibility inside browsers and encrypted sessions, leaving organisations blind to their fastest-growing data loss vector.

“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.”

Sathe says that Nightfall closes the real-time data exfiltration gap with an AI-native security architecture that operates directly at the browser, endpoint and SaaS layers i.e. where modern exfiltration actually happens, enabling real-time prevention before sensitive data ever leaves the organisation.

The shape of modern workflows

Analysts watching this space suggest that shadow AI has already outpaced legacy security architectures that were designed for a pre-AI world. Back in the day (as in, perhaps half a decade ago), sensitive data moved through email, file transfers and known SaaS applications… but today’s workflows look rather different.

In today’s modern workflows, we can say that:

• Proprietary enterprise application 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” (a set of special characters and syntax used to define text patterns for searching, matching, validating and manipulating strings in text) as well as network inspection and after-the-fact alerts, these workflows often go undetected… until sensitive data has already left the organisation.

CEO Sathe says that Nightfall’s AI-native approach was purpose-built to address these blind spots with features such as 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 – to provide real-time visibility into file uploads, clipboard paste actions, form submissions and screenshot-based sharing to any website or AI application. Content is analysed and blocked before transmission, without proxies, SSL inspection, or workflow disruption,” notes Sathe and team.

He further points to his platform’s endpoint coverage and notes that endpoint agents extend protection beyond the browser, monitoring cloud sync tools, desktop AI applications, Git and command line interface (CLI) operations, USB transfers, printing and clipboard activity across applications – all of which is claimed to close common workarounds used to bypass traditional DLP.

SaaS API enforcement 

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.

In terms of other functions to mention here, let’s note that AI-powered detection is built for context, not patterns and all enforcement layers are powered by Nightfall’s AI-native detection engine, which has the following functions:

• 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,” said Sathe. “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.

Overall, the company is talking about what it calls its unified approach to allow organisations across all business verticals to safely enable AI adoption while maintaining the visibility, governance and control required in regulated and high-risk environments.