Traditional AI observability tools were built for model development, not legal defensibility. Learn why regulated industries need cryptographic proof, not just
Most AI observability and governance platforms on the market today were designed for a different purpose: helping data science teams monitor model performance during development and deployment. They track metrics like accuracy, latency, drift,and feature importance. These are valuable capabilities, but they do not solve the problem that regulated industries actually face.
When a regulator, auditor, plaintiff attorney, or appeals board asks what an AI system decided and why, organizations need evidence that meets a higher standard than "our logs say so." Traditional logs are stored in mutable databases. Anyone with administrative access can alter them. Log rotation policies can delete them. Database migrations can corrupt them. There is no mathematical proof that the log entry reflects what actually happened at inference time.
InferTrust™ (Patent Pending) creates cryptographically signed decision records at the moment of inference using device-bound keys. Each record is linked in a tamper-evident hash chain. Any modification, deletion, or reordering of records is mathematically detectable. Independent auditors can verify record integrity using the public key without any access to the organization's systems. This is the difference between logging and proof. See how InferTrust™ delivers verifiable AI evidence.