Runtime Decision Enforcement for AI Systems
Trampolyne AI applies enforcement at the moment an AI system decides to act - inline, before execution.
Leverages existing assets for generating Policy-as-code
Policies define who can act, what can be accessed, and which tools can execute. Policy owners can define all this natural language.
Policy inputs
- IAM groups, roles, and entitlements
- Structured policy documents
- Natural-language authored rules
Access models & scope
- RBAC, ABAC, PBAC, NGAC
- User, application, data, tool scopes
- Composable and independent logic
Policy-as-code is enforced during pre-execution
Inline
Humans, services, and internal applications issuing AI requests
Runtime enforcement
LLMs, agents, tools, and downstream APIs
In Realtime
User, agent, data, tool, and session context
Model safety signals combined with organization-defined policy
Deviation from expected usage patterns
Allow, restrict, modify, or block
Simple integration
No SDK sprawl. No model rewrites. Trampolyne AI integrates as an API gateway or proxy layer. This allows teams to secure AI systems without refactoring models or workflows.