Secure Enterprise AI Without Exposing Sensitive Data.

TrustBound addresses the core governance problem that blocks regulated organizations from deploying AI at scale: every prompt sent to an external model is a potential data egress point. The platform sits as a proxy layer between an organization's users and AI providers, intercepting interactions in real time and replacing sensitive identifiers — PII, PHI, financial data, regulated content — with semantic placeholders before any request leaves the organization's perimeter. Models reason over the sanitized form; the original data is restored on the return path so end users see complete, meaningful responses.

The platform ships in two forms. TrustBound Chat provides a secure, familiar AI interface for executive and operational teams handling confidential information, with role-based access control, policy enforcement at inference time, and long-term audit logging. TrustBound API is a runtime governance layer for companies building AI-powered applications in regulated environments, offering cryptographically verifiable audit trails and production-grade latency alongside inference-time data protection. Guards — configurable rules defined in plain language — govern what each layer detects and how it responds to both requests and responses.

TrustBound's differentiating claim is that enforcement happens at runtime, not after the fact. Rather than scanning outputs for leaked data or relying on model-level fine-tuning, it tokenizes sensitive content before the model ever sees it. The resulting audit trail is immutable and built to satisfy SOC 2, HIPAA, and other enterprise compliance requirements. The platform targets regulated sectors including healthcare, financial services, government, insurance, and higher education — organizations that have been unable to move from AI experimentation to compliant production deployment.