Your policies say no. Your AI tools don't know that.
Every day, employees paste customer data, source code and internal documents into ChatGPT, Claude and Copilot. Your acceptable use policy doesn't stop it. Trampolyne does - enforcing your data governance rules at the exact point of AI interaction, in real time, across every surface employees use.
Your existing security stack
wasn't built for this attack
surface.
SIEMs record what happened. DLP catches file transfers. IAM controls who can log in. None of them intercept what an employee types into an AI prompt - and that's where the data is leaving.
When an employee pastes a customer contract into Claude to summarize it, nothing in your current stack stops it. The data has left your perimeter. It's been processed by a third-party model. You have no record of what was shared. And if a regulator asks, you have nothing to show. Trampolyne closes this gap - not with another policy document, but with runtime enforcement that sits between your employees and every AI tool they use.
Every AI surface employees use, governed in real time.
Trampolyne deploys as a combination of central proxy and endpoint utilities - covering every path data can take to an AI tool, with negligible perceived latency.
Converage for any time of web-based LLM usage = ChatGPT, Claude, Google Gemini, AI embedded in other apps. A browser extension intercepts and classify requests before they leave the browser.
Internal tools, SaaS integrations and developer workflows that call LLM APIs directly. Trampolyne intercepts at the API layer - covering automated pipelines, not just human-initiated prompts.
Model Context Protocol workflows where AI agents pull data from tools, databases and file systems. Trampolyne governs what data an agent can retrieve and send - before it acts.
When employees upload documents, spreadsheets, images or code files to AI tools. Content is classified against your data policies - not just by keyword, but by data type, provenance and sensitivity level.
Productivity-layer copilots embedded in Office, Slack, GitHub and other tools. These have direct access to internal data and can exfiltrate it silently. Trampolyne enforces policy at the plugin boundary.
AI agents that pull data from internal tools, files and databases can silently exfiltrate it through their pipeline to external models. Trampolyne classifies what the agent is sending outbound and blocks sensitive data before it leaves your environment.
Deploy once. Govern every AI interaction that follows.
No SDK required from your employees. No changes to how they work. No AI tools blocked wholesale - just the specific data transfers your policies prohibit.
Set up proxy layer + browser extension
Trampolyne listens at the proxy layer - either deploying our own proxy or integrating with your existing one. This covers all API-based, MCP and internal tool traffic. For web-based LLM usage (ChatGPT, Claude, etc.) a browser extension is also required. Both components are lightweight with negligible perceived latency.
Define your data policies
Specify what counts as sensitive: PII, source code, customer records, financial data, internal documents, regulated data. Import from your existing DLP classification or author rules via the dashboard. Natural-language policy authoring supported.
Traffic is classified in real time
Every prompt sent to an AI tool is classified against your policy before it leaves your environment. Sensitive data is blocked or redacted. Clean requests pass through transparently. Classification goes beyond keyword matching - considering data type, provenance and user role.
Full audit log + exception workflow
Every AI interaction is logged with classification result and policy decision - timestamped, queryable and exportable. Employees can request exceptions for legitimate business use via a built-in approval workflow. Full audit trail available at any time.
The audit evidence regulators
are going to ask for.
AI governance is moving from voluntary to mandatory. Trampolyne produces continuous compliance evidence - not a snapshot, not a report, not a checkbox. A live audit trail of every AI interaction in your organization.
GPAI obligations have applied since August 2025. Using ChatGPT, Claude or Copilot in your organisation makes you a deployer with governance, transparency and risk management duties. Trampolyne produces the usage logs and policy enforcement records those duties require.
Shadow AI creates unauthorized data processing - exactly what the DPDP Act prohibits. Trampolyne prevents data from being shared with unauthorized AI services and produces the processing logs that demonstrate compliance when enforcement arrives.
ISO 42001 requires documented risk management for all AI use, including employee-facing tools. Trampolyne's audit logs are the evidence this standard requires - every interaction, every policy decision, every exception, fully documented.
Several OWASP LLM categories - including sensitive information disclosure (LLM02) and excessive agency (LLM08) - apply directly to Shadow AI patterns. Runtime governance closes the gap between OWASP guidance and actual enforcement.
If you're responsible for data governance
and AI is already
in your org - this is for you.
You know employees are using AI tools. You have an acceptable use policy. But you have no enforcement layer and no audit trail. You need both - without blocking productivity or requiring a multi-year DLP replacement project.
You have a mandate to demonstrate AI governance. You need a system that produces auditable evidence continuously - not a one-time policy review or a snapshot assessment.
You want to enable AI productivity across the org. But you can't do that without knowing what data is flowing where. Trampolyne gives you visibility and control without requiring you to block AI tools wholesale.
Your users have company-issued AI tools and personal ones. Sanctioned tools and unsanctioned ones. Trampolyne governs all of them from a single control plane - without requiring you to block the unsanctioned ones at the firewall.
Your employees are using AI right now.
Do you know what they're sharing?
20 minutes is enough to understand your Shadow AI exposure and whether Trampolyne can close it.