Prompt and Circumstance: AI Is the New Attack Surface, and Browsers Enable It

Why the browser is now ground zero for data loss in the age of AI.

We are no longer easing into the era of AI. We now operate fully inside it, and this shift has changed the browser from a place where transactional data flowed to a place where converged AI prompt data accumulates and concentrates risk. The browser has always been one of the enterprise’s most exposed attack surfaces, but AI has intensified that exposure. Once just a gateway to the web, it is now the primary workspace for SaaS, cloud applications, and AI assistants. Employees upload files, share sensitive data in prompts, and manage credentials, all within browser tabs. Traditional network and email security cannot fully monitor these actions, which leaves organizations exposed.

Browser Data Loss Prevention (DLP) is designed to close this gap. It brings visibility and adaptive controls directly to the browser session, where modern work (and modern risk) happens.

Here are some of the top browser-based threats we’re seeing today, and how DTEX’s browser DLP addresses them. 

Top 3 browser-based data loss threats

1. Shadow IT and shadow AI

Shadow IT involves employees using unauthorized SaaS tools or personal accounts to get work done. Shadow Ai takes this a step further by introducing unapproved AI systems that can absorb, store, or learn from sensitive data. Together they expand the attack surface beyond what traditional controls can see.

AI accelerates innovation but introduces new risks. Teams adopt helpful tools fast. Employees increasingly use unsanctioned AI tools (Shadow AI) for coding and analysis, often exposing proprietary code and sensitive data on external platforms. These actions create untrusted locations where intellectual property and regulated data can escape organizational control. According to IBM’s Cost of a Data Breach Report 2025, incidents involving Shadow AI have led to IP and PII exposure, adding an average of $670,000 USD to breach costs compared to organizations with minimal or no Shadow AI usage.

Shadow IT compounds the problem. Personal email accounts, consumer-grade file sharing services, and other unauthorized SaaS apps provide convenient channels for data movement but bypass enterprise security controls. Employees often don’t realize that files they upload may contain embedded PII, PHI, or other compliance-controlled data. Without visibility at the browser level, organizations cannot distringuish between sanctioned and unsanctioned services, leaving critical blind spots. Browser-based monitoring closes this gap by detecting, blocking, and even redacting sensitive data before it reaches unauthorized destinations.

How browser DLP helps:

• Detect and block uploads or prompts containing sensitive data.
• Identify unsanctioned SaaS and AI usage directly in the browser.
• Apply adaptive policies differentiating between unauthorized and personal accounts.

2. Privileged user misuse

Privileged accounts provide access to sensitive systems and high-value data. When these users mishandle information, whether through negligence or intent, the impact can be severe. A common scenario: administrators download production datasets for troubleshooting, then upload them to personal cloud storage or AI tools for convenience. While these actions may seem logical in the moment, they create untrusted pathways for intellectual property and regulated data to leave the organization.

This risk is not theoretical. According to the 2025 Verizon Business Data Breach Investigations Report, breaches involving system administrators surged in 2025, accounting for 30% of all privilege misuse incidents. Insider actions, even those taken by trusted roles, can quickly escalate into major security events.

The challenge is compounded by the fact that privileged users often operate with fewer restrictions, making traditional security controls less effective. Without visibility into browser-based workflows, organizations cannot enforce guardrails where these actions occur. This blind spot turns routine administrative tasks into potential breach vectors, emphasizing the need for adaptive controls that respond to user role and context.

How browser DLP helps:

• Enforce stricter controls for privileged sessions.
• Block downloads from sensitive SaaS apps and uploads to unknown domains.
• Monitor session context without disrupting legitimate workflows.

3. Credential leakage

Credentials remain one of the most exploited assets in cyberattacks. Developers and employees frequently paste passwords, API keys, or tokens into online tools for quick formatting or debugging, unaware that these platforms often store or share content through public links. Once exposed, these secrets can be harvested by attackers or indexed by search engines, creating a persistent risk long after the initial action.

The danger doesn’t stop at accidental exposure. Stolen credentials are a leading cause of compromise, enabling attackers to bypass perimeter defenses and move laterally within environments. According to the 2025 Mandiant M-Trends Report, 16% of initial compromises were caused by stolen credentials. Exposed data becomes a prime target for attackers and, when credentials leak through browser-based workflows, they often evade traditional monitoring, leaving organizations vulnerable.

Preventing this requires real-time inspection of browser interactions to detect and block secrets before they leave the endpoint. By monitoring browser activity, organizations can close this critical gap and stop one of the most common (and costly) attack vectors.

How browser DLP helps:

• Detect and block secrets in text fields and uploads.
• Warn users when credentials are entered into untrusted sites.
• Monitor browser extensions for risky behaviors.

How DTEX addresses browser-based risks 

Protecting today’s browser-centric workflows requires understanding both user behavior and the data they interact with. DTEX provides this through behavioral intelligence, privacy-forward inspection, and real-time monitoring across browsers, SaaS tools, and AI assistants.

Identify browser-based data loss

DTEX provides deep visibility into browser activity through endpoint-local inspection, including Transport Layer Security (TLS) session analysis and the ability to review web session content when necessary. This inspection is privacy-forward: sensitive session content is analyzed only on the endpoint and never leaves the device unless it is confirmed as part of a data loss event. By monitoring uploads, downloads, and AI prompts, DTEX can detect proprietary and regulated information such as intellectual property, PII, PHI, and other compliance-controlled data before it escapes to unauthorized destinations.

Risk-adaptive DLP

Dynamic policies automatically adjust based on user behavior and session context, reducing false positives and allowing employees to remain productive without unnecessary friction. Instead of rigid, one-size-fits-all rules, DTEX applies adaptive controls that respond to real-world risk signals, ensuring security without slowing down innovation.

Visibility into shadow IT and shadow AI

DTEX gives CISOs and CIOs visibility into unsanctioned SaaS applications and AI tools being accessed across the organization. This insight helps security leaders understand their true AI risk posture, identify where sensitive data may be exposed, and implement governance strategies before incidents occur.

Server security

Privileged accounts and critical servers represent high-value targets for insider threats and external attackers. DTEX continuously monitors server workloads including bastion hosts, domain controllers, and application servers to detect abnormal activity such as unauthorized data transfers, disabling of security services, or suspicious command-line operations. By correlating endpoint and server behaviors, DTEX provides early detection of privileged misuse and insider risk without impacting server performance, helping organizations safeguard their most sensitive assets.

Privacy-forward by design

DTEX is built with privacy at its core. Metadata is collected and securely forwarded for analysis, while sensitive datasets such as web session content are inspected locally on the endpoint and never transmitted unencrypted. This approach ensures compliance is met with privacy standards while delivering more granular visibility needed to stop data loss at the source.

Moving beyond “good enough” 

AI and browsers have reshaped the enterprise attack surface in ways few anticipated. Every click, every prompt, and every upload inside a browser tab can carry risk — whether through shadow AI, unsanctioned SaaS, or careless credential sharing. Traditional security tools weren’t built for this reality. They can’t see what happens inside prompts or stop sensitive data from slipping through browser workflows.

That’s why browser DLP matters. It brings visibility and adaptive controls to the front line of modern work, where prompts meet circumstance. By inspecting interactions in real time, it prevents leaks before they happen, all without slowing down innovation.

Secure every interaction before it becomes a breach.

With AI now integrated in everything we do, every prompt has consequences. Assess your browser security posture so your organization can accurately map workflows, deploy adaptive controls, and inspect prompts before circumstances spiral.