"What I appreciated most was the approach - first-principles thinking, not just throwing an LLM at the requirement. Every finding came with clear proof, severity mapping to OWASP and LLM-specific threat models and actionable fixes prioritized by impact. When we look at Trampolyne's other offerings like the AI Governance Platform, we see even bigger value for us."
Head of Security, growth-stage companyFor security teams
You are responsible for AI security. Your current tools weren't built for it.
SIEMs, DLPs and IAM systems were built for deterministic software. AI behaves differently at runtime. Trampolyne gives security teams enforceable controls and audit evidence built specifically for AI.
The situation
AI is everywhere in your org. You have limited visibility and no real enforcement.
Security teams are being asked to sign off on AI projects before they go to production. The problem is they are being set up to fail.
Shadow AI is already happening
Employees are using public AI tools - web LLMs, API-connected copilots, MCP-enabled workflows - without going through IT or security. Source code, customer data and internal documents are leaving the perimeter with no record.
Policies exist. Enforcement doesn't.
Your acceptable use policy mentions AI tools. But when an employee pastes customer data into ChatGPT or uploads a document to Claude, nothing intercepts it. The gap between your policy and employee behaviour is where the risk lives.
No audit trail when something goes wrong
When an AI incident occurs - a data breach, a privilege escalation, a policy violation - you have no log of what the AI was asked, what it accessed and what decision it made. Investigations stall. Regulators ask questions you cannot answer.
Why existing tools don't solve this
The category gap that Trampolyne fills
This is not a problem you can solve by reconfiguring your existing stack.
SIEM and log aggregation
Records what happened. Cannot prevent it before it executes. AI decisions happen inside model inference - there is nothing to intercept at the log layer.
DLP and data classification
Designed for file transfers and email. Cannot evaluate whether a prompt sent to an LLM contains sensitive data or whether an AI output is about to leak internal information.
IAM and access control
Controls who can log in. Does not control what the AI can do on their behalf, what data it can pull from connected systems or how broadly it interprets a user request.
What Trampolyne provides
Enforcement before AI acts. Evidence after.
One product. Three things it gives you that your current stack cannot.
Shadow AI Controls
Enforce data governance at the point where employees interact with AI tools. Works across web LLMs, LLM APIs and MCP surfaces. Blocks sensitive data from being shared based on your classification rules. Logs every interaction for audit.
What it covers: text, documents, images, code. Goes byeond content to consider provenance. Exception workflows for legitimate business use.
Coverage across every AI surface employees use
Works across web-based LLMs (ChatGPT, Claude, Copilot), API-connected tools and MCP-enabled workflows. Evaluates text, documents, images and code in context — not just by keyword match. Exception workflows for legitimate business use.
Goes beyond content to consider provenance, user role and data classification before making a policy decision.
A complete audit trail for every AI interaction
Every prompt, every data transfer and every policy decision — logged, timestamped and queryable. When an incident is investigated, a regulator asks for evidence or a data handling dispute arises, you have the full record.
What gets logged: what was shared, by whom, which policy triggered, what was blocked or allowed and why.
Regulatory and standards context
The audit evidence regulators and customers are going to ask for
AI governance is moving from voluntary to mandatory. Trampolyne produces the evidence your compliance and legal teams need - not retroactively, but continuously.
OWASP LLM Top 10
Red-team findings map directly. Enforcement logs show runtime controls against the same categories.
EU AI Act
GPAI obligations have applied since August 2025. Using ChatGPT, Claude or Copilot
in your organisation makes you a deployer with governance and transparency duties.
India DPDP Act
Shadow AI controls prevent data processing by unauthorized AI services. Logs provide evidence of data handling.
ISO/IEC 42001
Requires documented AI risk management for all AI use — including employee-facing tools.
Shadow AI Controls logs are the audit evidence this standard asks for.
From design partners
What security practitioners said
Trying to get ahead of an AI security mandate or incident?
Whether you already know Shadow AI is happening in your org or you are trying to get ahead of a DPDP or EU AI Act obligation — 20 minutes is enough to understand if we can help.