Trust Infrastructure for Enterprise AI
Know where your model's output came from, what data shaped it, and whether it can be trusted.
The Problem
Organizations are adopting AI rapidly but today's systems are not keeping up with the regulatory pressures, audit, and accountability requirements. The result is a dangerous visibility gap at the heart of enterprise AI.
AI systems produce outputs that cannot be traced back to source data, model versions, or decision logic. When something goes wrong, there's no audit trail. When regulators ask questions, there are no answers.
DORA, the Colorado AI Act, EU AI Act, and other emerging cybersecurity frameworks assume organizations can demonstrate the lineage of AI-driven decisions. Most cannot and these regulatory frameworks are becoming law in just a few months. The gap between regulatory expectation and operational reality is growing.
AI systems within your organization are producing content and guiding decisions faster than any governance process was designed to handle. We want to help teams succeed in their AI adoption journey by creating an environment where they can move fast and not break things.
The shift to AI native org structures will not happen overnight and there will be a gradual shift to fully autonomous systems. Security and risk leaders are the last line of defense against AI adoption moving faster than governance. Without verifiable trust signals, the safest answer is always to slow down or say no.
Our Approach
GraphMachine builds a verifiable record of AI system behavior. A trust layer that sits beneath your existing AI systems and surfaces lineage, attribution, and accountability signals.
Instrument your AI pipelines to record data provenance, model versions, and inference events as they happen
Represent how your team is interacting with AI systems to create high-stakes artefacts like code, documents, and reports.
Surface trust signals, anomalies, and compliance evidence in your everyday work. And store compliance evidence for your security teams and auditors.
For Security Leaders
We are in active research with CISOs and CIOs across financial services, compliance, consulting, healthcare, critical infrastructure, and the public sector. These are the patterns we hear most often.
Regulatory
Demonstrate to regulators exactly which data informed a model's output — meeting the evidence standards of EU AI Act, DORA, and internal risk frameworks without manual reconstruction.
Risk
Know when a third-party model version changes, when training data provenance is unclear, or when a vendor pipeline introduces unknown data into your production systems.
Operations
When an AI-driven decision causes harm, answer the question: "why did the model do that?" with a verifiable, timestamped lineage record rather than a post-hoc guess.
Governance
Bind AI usage policies to provenance data — automatically restrict or escalate based on what you can actually verify, not what vendors claim about their own systems.
Adoption
Security's role shifts from gatekeeper to enabler when trust is verifiable by design. Move faster on AI adoption because you have the evidence base to back it.
Posture
Replace point-in-time security assessments with a living record of AI system behavior; provenance data that updates in real time as your AI landscape evolves.
Research Partnership
We're in early-stage research and are speaking with CISOs, CIOs, and security architects to understand the provenance and AI trust problems that matter most. No sales. Just questions, and building something together.
Research participants. Invitation only. No vendor pitch.