Introducing Sentra Shadow AI DLP: Extending AI Data Readiness to the Browser

Enterprise AI has created a data security challenge that existing security architectures were never designed to solve. Sensitive business information no longer leaves the organization exclusively through email, file transfers, or cloud storage. Today it increasingly leaves through prompts, pasted text, and uploaded documents shared with ChatGPT, Claude, Gemini, Microsoft Copilot, and hundreds of other AI applications employees adopt on their own.

Every interaction with an AI application is now a potential data egress event.

Unlike traditional data movement, these interactions happen in milliseconds, leaving security teams with little or no opportunity to intervene after the fact. Protecting enterprise AI therefore requires security decisions to happen before sensitive information reaches the AI application.

Traditional DLP solutions were designed to inspect email, network traffic, removable media, and file transfers. They were never built to understand browser-based AI interactions or determine whether the information being shared is actually important to the business.

Today, we're introducing Sentra Shadow AI DLP, a new capability within the Sentra AI Data Readiness Platform that extends Sentra's data intelligence into the browser. Shadow AI DLP combines Shadow AI Discovery and AI Browser DLP, giving organizations visibility into AI usage while preventing sensitive data from being shared with AI applications before it leaves the browser.

This launch is about more than adding browser protection. It reflects our belief that AI has fundamentally changed how enterprise data moves, and organizations need a fundamentally different approach to protecting it.

AI has created a new category of data movement

Every major technology shift has forced security teams to rethink how they protect enterprise information. Cloud computing changed where data was stored. SaaS changed how employees accessed applications. Generative AI is now changing how employees interact with enterprise data itself.

Unlike traditional applications, AI systems are conversational by design. Employees no longer need to download a document, attach it to an email, or upload it into a file-sharing service to move information outside the organization. Instead, they simply copy information into a prompt or upload a document directly into an AI application. Within seconds, that information can be analyzed, summarized, translated, rewritten, or incorporated into downstream AI workflows.

From an employee's perspective, this is exactly what makes AI valuable.

From a security perspective, it creates an entirely new path for sensitive data to leave the enterprise.

Browser-based interactions with AI applications represent a workflow that existing security controls were never designed to inspect or govern. As AI adoption accelerates, organizations find themselves with a rapidly expanding blind spot at precisely the point where employees are interacting with sensitive business information.

This is not simply another variation of shadow IT.

When employees adopted unauthorized SaaS applications, security teams could often discover the application, understand where data had been stored, and take corrective action. AI interactions happen differently. Sensitive information can be copied into an AI prompt, processed immediately, and become part of an interaction before security teams are even aware it occurred. The opportunity to intervene often exists for only a fraction of a second.

Visibility is only the beginning

Most organizations start with a simple question: Which AI applications are our employees using?

Answering that question is more difficult than it sounds. Employees adopt new AI tools faster than security teams can evaluate them, making static allowlists obsolete almost as quickly as they are created. Visibility is an essential first step, but it does not reduce risk.

Organizations also need to decide which AI applications are approved, what information can be shared with them, and how those policies should be enforced consistently.

Shadow AI Discovery surfaces every AI application in use across the organization — sanctioned or not — with its category, active user count, last activity, and a computed risk score. That score is not a static vendor rating pulled from a catalog. It combines Sentra's own vendor-posture research on how each AI application handles, retains, and trains on data with the sensitivity of the data your users have actually sent to it. Vendor risk, weighted by real exposure.

Discovery is also not restricted to a predefined list of known AI tools. Because employees adopt new applications faster than any catalog can be maintained, Sentra continuously monitors the AI applications actually in use and keeps usage metrics and risk scores current automatically, with no manual sync or publishing step. Admins can bulk-sanction or unsanction applications, filter by risk, category, or user count, and move from a flagged application directly into an enforcement policy.

AI Browser DLP then enforces policy at the moment of paste or upload, before content reaches the AI provider. Every event is classified locally against the data classes your organization already uses across Sentra — Patient Records, Source Code, customer PII, and any custom classes you've defined — and policy is a two-axis decision: which classes you protect, crossed with how each application should enforce.

• Block — the paste is suppressed, with a notification explaining what was detected
• Justify — the user must enter a written, logged reason before proceeding
• Verify — a lightweight confirmation, logged
• Allow — the paste proceeds and the decision is logged, useful during policy tuning
• No enforcement — the class is ignored for that application

When a paste is blocked, the employee sees a clear, non-punitive explanation with a one-click path to request review. The extension stays out of the way otherwise.

AI security begins with understanding the data

As organizations evaluate browser security for AI, one question matters more than any other:

How does the platform decide whether sensitive information should be allowed to leave the organization?

Most DLP products make that decision using only the information available at the endpoint. They inspect content using keywords, regular expressions, predefined patterns, or exact matches without understanding whether the content is actually important to the business. That approach inevitably creates false positives, false negatives, and continual policy tuning because the endpoint lacks business context.

Generative AI changes that assumption.

Employees rarely copy entire documents into AI applications. Instead, they ask AI to summarize reports, explain source code, rewrite customer communications, or analyze financial data. Information can be paraphrased, restructured, or translated while still retaining its business value. 

Sentra starts from a fundamentally different place.

The AI Data Readiness Platform has already spent years discovering, classifying, and governing enterprise data. Before an employee ever pastes information into an AI application, Sentra already understands what the data is, where it lives, who owns it, how sensitive it is, and which governance policies already apply to it.

Shadow AI DLP extends that same intelligence into the browser, allowing enforcement decisions to be driven by business context rather than pattern matching alone.

Instead of creating a separate classification engine, AI Browser DLP uses the same business-aware data classifications that organizations already trust throughout the Sentra platform. The browser becomes the enforcement point, while the AI Data Readiness Platform remains the source of truth for understanding enterprise data. Because enforcement decisions are driven by business context rather than simple pattern matching, organizations can significantly reduce false positives, reduce policy tuning, and improve detection accuracy, even when content has been reworded or restructured.

This is why we see DSPM and DLP as complementary technologies rather than competing ones. DSPM provides the intelligence. Browser DLP extends that intelligence to the point where data is actually leaving the organization through AI.

Protecting data should not require compromising privacy

Whenever browser-based security is introduced, an important question follows.

What happens to employee prompts and uploaded content?

We believe that concern deserves a clear answer.

Shadow AI DLP was designed so that the content employees paste or upload never leaves their device. Classification happens locally on the endpoint before information reaches the AI application, and the browser continues communicating directly with the AI provider. Sentra never receives the original prompt or document, nor do we build a repository of employee AI conversations.

Instead, the platform receives only the information necessary to enforce policy and support governance, including the matched data classification, the AI application, the user identity, and the enforcement action. Policies are cached locally, allowing enforcement to continue even if a device temporarily loses connectivity.

This architecture gives organizations the ability to govern AI interactions without introducing another browser proxy, creating unnecessary latency, or collecting employee conversations. It also reinforces an important principle: the goal is to help organizations use AI safely, not to monitor how employees work.

AI governance is becoming a business requirement

Increasingly, boards, regulators, and customers are no longer asking whether organizations have an AI policy. They want evidence that the policy is actually being enforced.

Boards want assurance that AI initiatives comply with regulatory requirements. Security leaders need confidence that proprietary information is not unintentionally shared with third-party AI services. Data governance teams need consistent policies that extend across both traditional applications and emerging AI platforms.

These concerns are reflected across the industry. Gartner recently predicted that through 2026, organizations will abandon more than 60% of AI projects that are not supported by AI-ready data, reinforcing that successful AI initiatives depend not only on powerful models but also on trusted, governed, and well-understood data. AI readiness has quickly become a prerequisite for AI success, rather than an operational consideration addressed later.

Shadow AI DLP addresses one of the most immediate governance challenges organizations face today by protecting browser-based interactions with AI applications. At the same time, it reinforces a broader principle that has guided the Sentra platform from the beginning: organizations cannot secure AI unless they first understand the data AI can access.

Extending the AI Data Readiness Platform

Although today's announcement introduces a new browser-based capability, we see it as a natural extension of the AI Data Readiness Platform rather than a new product direction.

Over the past several years, Sentra has focused on helping organizations continuously discover, classify, govern, and protect sensitive data wherever it resides. As AI adoption has accelerated, it became clear that the same intelligence organizations rely on to secure data at rest should also guide how that data is used by AI applications.

Shadow AI DLP extends our platform into one of the fastest-growing channels for enterprise data exposure while remaining true to the architecture that differentiates Sentra. Instead of building another standalone DLP platform, we are extending business-aware data intelligence to wherever employees interact with AI and enabling organizations to apply consistent governance across every stage of the data lifecycle.

We are also deliberate about scope. Shadow AI DLP is a focused control for a specific, urgent gap, not a replacement for email, network, or full endpoint DLP. Our most durable advantage is classification quality, not enforcement infrastructure in every channel — so our primary strategy is partnership. We are working with leading next-generation DLP vendors to let them pull Sentra's data intelligence directly into their own endpoint agents, and for organizations running legacy stacks built around Microsoft Purview and similar platforms, Sentra automatically applies the sensitivity labels those tools already know how to enforce against. Our browser control serves organizations that need protection at the AI channel today; the rest of the enforcement ecosystem gets sharper because the intelligence underneath it is better.

Looking ahead

AI is changing how organizations create, access, and share information. Every prompt, every pasted paragraph, and every uploaded document has become a potential data movement event, and every interaction requires a security decision.

As Yair Cohen, Co-founder and Chief Product Officer at Sentra, explains:

"As AI becomes embedded in everyday work, organizations need to govern not just which AI applications employees use, but what data they're sharing with them. Traditional DLP solutions make decisions based on what they see at the point of enforcement. Shadow AI DLP extends the intelligence of our AI Data Readiness Platform into the browser, applying business context to every policy decision. The result is more accurate detection, fewer false positives, and the confidence to adopt AI without putting sensitive data at risk."

Shadow AI DLP is the first extension of that vision into the browser. It will not be the last.

See it in your environment. Shadow AI Discovery will show you which AI applications your employees are using, and what data has already been shared with them, within days of deployment.

Request a demo · Learn how AI agents are reshaping enterprise data security in our CISO's Guide to Agentic AI Data Exposure