Mammoth CyberArtificial intelligence is transforming how we work — and once again, the browser is at the center of it all. With over 85% of employees’ work happening inside the browser, the industry is now embedding AI assistants directly into it, giving rise to a new generation of AI browsers that promise smarter, faster, and more connected work.
From summarizing reports to automating workflows, AI browsers unlock enormous productivity gains. It’s no wonder tech giants and startups alike are racing to release their versions. But amid this excitement, there’s a dangerous blind spot: AI browsers introduce new and unseen security risks that traditional cybersecurity tools were never designed to handle.
In a traditional browser, security threats revolve around phishing, malicious scripts, or stolen cookies. But once AI becomes part of the browser, the threat model changes completely
An AI browser isn’t just showing you data — it’s reading, interpreting, and acting on it. This makes the browser an active participant in your workflow — and a new attack surface for adversaries.
| Category | Traditional Browser | AI Browser |
| Primary Function | Displays content | Displays content Interprets and acts on content |
| Attack Vector | Malicious javascript, Malicious extensions, Data leakage | Prompt injections, Model hijacking, Data leakage |
| Data Exposure | Cookies, stored credentials | Prompts, AI context, internal data, workflows |
| Security Model | Endpoint-based | AI governance Central management Browser-level policy |
When AI becomes part of the browser workflow, the same webpage content can become the instructions to the AI assistant. This shift calls for a new security model — one that monitors and governs data flows, AI behavior, and context boundaries within the browser itself.
Here are the key security risks enterprises must understand before deploying AI browsers at scale:
a. Indirect Prompt Injection
Indirect prompt injection is a sophisticated attack where malicious instructions are hidden within the data sources to an LLM model, where LLM model treats external contents as the instructions to execute malicious activities. LLM models are designed to process all text as meaningful input. These LLM models lack a built-in mechanism to separate instructions from regular information, so if a command is embedded within seemingly normal content (such as an email, document, or web page), the AI may interpret it as a genuine instruction.
Indirect prompt injection is the most critical threat for LLM models. This vulnerability expands the attack surface of AI-enabled browsers because almost any integrated data source can be weaponized, making robust data controls and continuous monitoring crucial for secure deployment in enterprise settings. The AI browsers must have built-in protection mechanism to mitigate the threats
b. Expanded Endpoint Exposure
Traditional browsers store cookies, tokens, and metadata in the endpoints. AI browsers may also store additional session context, temporary memory, or cached model parameters on local devices. If an endpoint is compromised, these stored elements become valuable targets for attackers. The design of AI browsers should be based on zero-trust principle to prevent the data theft or alternation of these critical data.
c. Data Leakage Through AI Prompts
Employees often share internal information — contracts, source code, financial data, or customer records — when interacting with AI assistants embedded in the browser. Unlike traditional tools, AI models process natural language prompts that may inadvertently include confidential details. Once submitted, this data could be transmitted to external AI services for processing, stored in model logs, or even used for further model training. Without strict governance and data boundaries, sensitive business information can easily flow outside the enterprise perimeter, often without the user’s awareness.
The risk goes beyond accidental disclosure. Malicious actors could exploit AI prompts or injected instructions to exfiltrate data intentionally — for example, by manipulating the AI into emailing confidential files or sending snippets of source code to unapproved endpoints. Such incidents may breach corporate confidentiality. A guardrail for the AI browsers must be implemented to limit what the AI browser can see and access.
d. Shadow AI and Model Misuse
When IT teams can’t see which AI tools or models that employees are using, “shadow AI” emerges — untracked, unapproved, and risky. This lack of visibility makes it impossible to enforce data boundaries or ensure that sensitive information isn’t misused.
The AI browser introduces a fundamentally new operating environment — one that merges external web content, untrusted endpoints, unknown user behavior, and now, powerful AI capabilities that can read, interpret, and act on enterprise data. Traditional network or endpoint-based tools were never designed to understand or inspect these AI interactions. They cannot see what information is passed into prompts, how AI models process it, or what data is generated in response. This lack of visibility creates a dangerous blind spot, leaving SOC teams unable to detect data misuse, prompt injections, or unapproved model activity happening entirely within the browser itself.
To address this gap, organizations need a new security model that integrates AI governance directly into the browsing environment. Such a model must continuously monitor data flows, enforce contextual boundaries, and regulate how AI models access and interact with enterprise information — ensuring visibility, accountability, and control without hindering productivity.
In the enterprise world, AI without governance is a liability. That’s why the next generation of AI browsers must be secure-first by design — building protection into the browser core before any AI model runs.
The Mammoth Enterprise AI Browser was created precisely for this need. It’s built for security, designed for AI :
a. Secure-First Architecture:
Every AI workflow in Mammoth runs within a tightly controlled environment separating from browser core, ensuring that data governance, DLP, and identity verification occur before any information reaches an AI model. This architecture minimizes risk by enforcing corporate security policies at the browser core, safeguarding sensitive data at the very first layer of interaction.
b. AI Governance Layer:
Mammoth includes a built-in governance framework that determines which AI models can access specific data and under what conditions. With embedded prompt filtering, data masking, and observability features, it prevents unauthorized exposure of sensitive information while maintaining full auditability for compliance and security teams.
c. In-Browser DLP:
Unlike traditional DLP solutions that focus only on file movement, Mammoth’s inbrowser DLP extends protection to user actions such ascopy/paste, screenshots, and uploads. This ensures sensitive data cannot be extracted or shared, securing the last mile where most data leaks actually occur.
d. Indirect Prompt Injection Defense:
Mammoth proactively detects and isolates untrusted or malicious sources of the contents before it interacts with the embedded AI assistant. By inspecting web data and user prompts in real time, it prevents hidden instructions or manipulative inputs from hijacking AI behavior, safeguarding both users and corporate systems from emerging AIspecific threats.
e. Zero-Trust Access:
Mammoth eliminates the need for legacy VPN or VDI setups by providing zero-trust access directly from the browser to SaaS and private applications. Each session is identity-verified and context-aware, ensuring secure connectivity for remote and hybrid workers with zero trust principles without compromising speed, usability, or compliance.
Browsers are rapidly becoming the new operating environment for modern enterprises, serving as the interface where employees access data, applications, and now, AI-powered insights. However, without built-in security, these same tools can become powerful attack surfaces —exposing sensitive information, misusing AI prompts, or allowing unmonitored data exchanges. The goal isn’t to halt innovation or slow AI adoption, but to build a secure foundation that ensures AI can be used responsibly, transparently, and safely across all workflows.
Mammoth Enterprise AI Browser is purpose-built to fulfill this need. With a secure-first architecture, integrated AI governance, and in-browser data protection, it empowers organizations to harness AI’s full potential without compromising control or compliance. Mammoth redefines the browser as a trusted security boundary — one that enables productivity and innovation while keeping sensitive data, users, and models under enterprisegrade protection. In theAIera,securitytruly begins in the browse
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