Trampolyne AI Governance Platform

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Trampolyne AI sits between AI and it's users to ensure your data and systems are protected against unintended actions at runtime. - AI can be home-grown AI applications or any public LLM tools - LLM chat, LLM APIs, MCP and so on. - Users can be human or other systems Trampolyne AI goes way beyond prompt filtering or LLM against LLM. It's build with low-latency, high-fidelity, multi-layer architecture that enables deterministic control over a fundamentally underministic system - AI. The platform enables enterprise-grade audit trails with clear, focussed actionables.

Trampolyne Shadow AI Governance Platform

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Trampolyne Shadow AI Governance enforces what employees can and cannot share with public AI tools. It works across across web LLMs, LLM APIs, and MCP-enabled workflows before data leaves the organisation. Unlike DLP tools built for email and file transfers, Trampolyne evaluates AI-specific surfaces in context: it assesses text, documents, images, and code not just by content match but by provenance, user role, and data classification before making a policy decision. When a sensitive data transfer is blocked, the interaction is logged with full context - what was shared, by whom, which policy triggered, what was blocked, and why. Exception workflows let security teams handle legitimate business use without broad exemptions. Every interaction is logged, timestamped, and queryable for incident investigation, regulatory audit, or data handling disputes.

Trampolyne Enterprise AI Governance Platform

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Trampolyne Enterprise AI Governance sits inline between users and production AI systems, enforcing what the AI can access, do, and share on every request before the model or tool executes. Policies are generated from existing IAM entitlements, policy documents, and business rules and versioned for audit. Enforcement covers model prompts, retrieval context, tool calls, outputs, and approval paths. Per-request decisions check user identity, data sensitivity, intended action, tool scope, and behavioural risk in milliseconds. Actions are then allowed, blocked, redacted, or routed for exception handling. Supports RBAC, ABAC, PBAC, and composable hybrid policy models. Integrates as an API gateway or proxy - no model rewrites, no SDK changes, typically ships in days. Every policy decision is logged with actor, context, decision path, and versioned rule history. This provides security teams, compliance functions, and incident response teams with a complete, auditable record. This is useful for internal reviews, customer security questionnaires, EU AI Act readiness, and forensic investigation. Built for internal copilots, tool-using agents, customer-facing AI, and shared governance programmes across security, platform, and product teams.