An AI model reads a radiology scan and says no cancer detected. Two years later, the patient has cancer. Which model version made that call? How confident was i
Picture this scenario: an AI model reads a patient's radiology scan and concludes no cancer detected. Two years later, that patient is diagnosed with cancer. Their legal team asks four simple questions:
Today, no system in production can answer all four questions with cryptographic proof. The records exist in JSON files, databases, and log aggregators. But none of them carry their own evidence of authenticity. None of them can prove they were not modified between the moment of inference and the moment of audit.
This is not a hypothetical problem. It is the central gap in AI governance that every regulated industry will face as AI-assisted decisions become the norm rather than the exception.
The gap exists because AI systems were built for performance, not provability. When engineers design an AI pipeline, their focus is on model accuracy, latency, throughput, and uptime. Logging is an afterthought, typically implemented as a JSON write to a database or a call to a log aggregation service.
This approach works for debugging and monitoring. It does not work for legal defensibility or regulatory compliance. The fundamental issue is that the same system generating the decision also controls the record of that decision. It is like a student grading their own exam.
Organizations deploying AI in regulated environments face growing exposure. Healthcare systems using AI for triage cannot prove which model version flagged or cleared a specific case. Financial institutions using AI for credit decisions cannot demonstrate to examiners that the decision record reflects what the model actually produced. Autonomous vehicle manufacturers cannot reconstruct exactly what the safety system decided in the seconds before an incident.
In each case, the absence of cryptographic proof creates liability. Not because the AI made a wrong decision, but because the organization cannot prove the AI made the decision it claims.
InferTrust™ was built to close this gap. By signing a cryptographic receipt at the exact moment of inference, before any network call, UI update, or application process can observe or alter the output, InferTrust™ creates verifiable proof of what was decided, when, by which model, under which policy, and with what confidence level. The result is a decision record that carries its own evidence of authenticity, answering all four questions with mathematical certainty.