Real-world consequences organizations face when they cannot produce verifiable proof of AI decisions during audits, litigation,and regulatory investigations.
Organizations deploying AI in regulated environments without cryptographic decision records face concrete, measurable risks that grow as AI adoption accelerates.
When regulators investigate AI-assisted decisions, they expect verifiable evidence. Organizations that can only produce application logs face an immediate credibility gap. The regulator must trust that the logs are complete, authentic,and unmodified. If the organization has any history of data quality issues, system outages, or administrative access incidents, that trust erodes quickly. Fines, consent decrees,and operational restrictions follow.
In litigation involving AI decisions (denied claims, adverse clinical outcomes, product defects), opposing counsel will challenge the integrity of decision records during discovery. Logs stored in mutable databases are vulnerable to the argument that they could have been modified after the fact. Without cryptographic proof, organizations spend significant legal resources defending the authenticity of their own records.
Accreditation bodies (URAC, Joint Commission, ISO registrars) increasingly evaluate AI governance as part of their audit processes. Organizations without verifiable AI decision records may face findings, corrective action requirements, or accreditation risks that affect their ability to operate.
The cost of implementing cryptographic decision records before an incident is a fraction of the cost of responding to a regulatory investigation, defending a lawsuit, or remediating an audit failure without them. InferTrust™ exists because the stakes of AI decisions are too high for "we have logs" to be the answer.