Real Examples

How companies use Trampolyne: real situations

Find your situation. See how Trampolyne addresses it.

Every scenario below needs runtime enforcement, not post-facto audit.

For AI builders

Enterprise procurement blocked by AI security questions

AI-native SaaS teams can lose enterprise deals when they cannot prove governance with concrete evidence.

Context: Procurement asks for AI risk evidence
Risk: Deal stalls or dies despite product fit
What Trampolyne does: Red-team report + runtime governance evidence for enterprise review
What you get: Vulnerability report with severity, remediation and audit-ready logs
Enterprise procurement blocker use case
For enterprise security teams

Shadow AI systems (Unmanaged enterprise tools)

Departments and teams deploy unsanctioned AI tools, often bypassing security controls and creating untracked data flows.

Context: Unmonitored AI tool adoption spreads
Risk: Sensitive data exfiltration to external AI services
Failure mode: No visibility into data handling
Runtime requirement: Enforce data governance at point of use
Use-case 4: Shadow AI systems monitoring dashboard
For enterprise security teams

Internal AI copilots (Employee-facing assistants)

Copilots used by engineers, analysts and operators often have broad access to internal data and tools.

Context: Employee queries internal systems
Risk: Overbroad data exposure or misuse
Failure mode: Static controls miss intent
Runtime requirement: Decisions must be enforced per request
Use-case 1: Internal AI copilot dashboard
For AI in production

Operational agents (Tool-enabled AI workflows)

Agents with access to infrastructure, cost or financial APIs can trigger high-impact actions.

Context: AI invokes operational tools
Risk: Unauthorized or unsafe actions
Failure mode: Prompt-level controls insufficient
Runtime requirement: Tool calls must be policy-gated
Use-case 2: Ops and finance agents policy creation UI
For AI in production

Customer support AI (Externally influenced systems)

Support agents interact with untrusted user input while accessing internal systems.

Context: External user drives AI behavior
Risk: Data leakage or role escalation
Failure mode: Training-time guardrails fail
Runtime requirement: Contextual enforcement required
Use-case 3: Customer support AI policy creation UI

Common risk patterns we protect against

Unauthorized data access

AI accessing data beyond user or role intent

Unauthorized data sharing

Internal users sharing data & access with public AI tools

Improper tool invocation

Unsafe or unintended API calls

AI Behavioral drift

Gradual deviation from expected usage

Policy circumvention

Attempts to bypass enforced constraints