For AI in production

Your AI is live on real data. Prompts and model settings are not real guardrails.

If your AI agent, copilot or workflow has access to production data and tools, you need enforcement before it acts — not just logs after the fact. Trampolyne sits inline between your users and your AI system, enforcing what it can access, do and share in real time, before anything executes.

The situation

Production AI creates a new control plane problem

Traditional app security tells you who signed in. It does not decide what an AI can access, which tools it may call or whether a response is about to expose sensitive information.

Copilots see too much

Internal copilots often sit on top of broad knowledge sources - docs, tickets, source code, data stores. Without per-request policy checks, users can get answers they were never meant to see.

Agents can take unsafe actions

Tool-enabled agents can trigger workflows in CRM, finance, support or infrastructure systems. A single unsafe instruction can turn into a real operational incident.

Logs alone are too late

Post-facto observability helps investigations. It does not stop a data leak or bad tool call that already happened. Runtime control has to sit inline before execution.

What Trampolyne does

Inline governance for production AI systems

Trampolyne sits between the requester and the AI system, evaluates every request against policy, then allows, blocks or modifies before the model or tool executes.

Generate policy from what you already have

Import IAM entitlements, policy documents and business rules. Convert them into versioned, testable AI policies without starting from a blank page.

Models supported: RBAC, ABAC, PBAC and composable hybrid policies.

Enforce before any AI action executes

Check identity, data sensitivity, intended action, tool scope and behavioral risk in milliseconds. Then allow, block, redact or route for exception handling.

Covers: model prompts, retrieval context, tool calls, outputs and approval paths.

Keep audit-ready evidence on every decision

Every policy decision is logged with actor, context, decision path and versioned rule history. That gives security, compliance and incident response teams something they can actually use.

Useful for: internal reviews, customer security questionnaires, EU AI Act readiness and forensic response.

What changes after

You move from AI observability to AI control

The difference is simple: the system no longer just tells you what the AI did. It constrains what the AI is allowed to do.

Per-request decisions
Access is enforced on each request using user role, data class, tool scope and runtime context. Static permissions stop being your only line of defense.
Safer production rollouts
Teams can ship copilots and agents with enforcement in place from day one, instead of hoping prompts and instructions will hold under real user behavior.
Faster investigations and reviews
When an incident or audit happens, you can show what request came in, what policy fired, and why the action was allowed, blocked or changed.
Typical production scenarios

Where teams use this first

Internal copilots
Prevent over-broad retrieval from internal docs, codebases, tickets and knowledge systems.
Tool-using agents
Gate calls to finance, support, CRM, infrastructure and workflow automation systems.
Customer-facing AI
Reduce prompt injection, unsafe outputs and policy violations where external users can influence the model.
Shared governance programs
Give security, platform and product teams a single enforcement layer instead of fragmented controls.

Running AI in production already?

If the answer is yes, runtime governance should not be a Q4 project. We can show what inline enforcement would look like in your environment in one call.