AI observability platforms monitor model performance, detect drift, and surface operational issues. InferTrust™ creates cryptographic proof of what each AI mode
AI observability platforms (such as Arize, Fiddler, WhyLabs, and Datadog ML Monitoring) are designed to help ML teams monitor model performance in production. They track metrics like prediction drift, feature importance changes, latency, throughput, and data quality. They are valuable operational tools that help engineering teams keep models running well.
InferTrust™ solves a fundamentally different problem: proving, after the fact, exactly what an AI model decided at a specific moment in time, with evidence that holds up under regulatory scrutiny, legal discovery, and adversarial audit. This is not a monitoring question. It is an evidentiary question.
Observability platforms aggregate metrics. They show trends, distributions, and anomalies across many predictions. But they do not create an individual, tamper-evident record for each inference that proves the exact model version, confidence score, policy state, and input features for that specific decision. They cannot prove that a record was not altered after creation. They cannot function offline on edge devices. And they were not designed to produce evidence that satisfies regulators, auditors, or courts.
InferTrust™ operates at the individual inference level. Every time your model produces an output, InferTrust™ creates a signed decision record sealed with device-bound cryptographic keys before any network transmission occurs. This record is not a log entry in a database that could be edited. It is a cryptographic receipt that is mathematically verifiable by any third party. The append-only log structure means deleted or reordered records leave visible gaps, proving the log is complete.
InferTrust™ and observability platforms are complementary. Use observability to monitor whether your models are performing as expected. Use InferTrust™ to prove what each model actually did when a regulator, auditor, or court asks. Many InferTrust™ customers run both: observability for operations, InferTrust™ for accountability.
Observability platforms write records to mutable databases. InferTrust™ creates cryptographically signed, append-only records. Observability requires network connectivity. InferTrust™ works offline on edge devices. Observability aggregates across predictions. InferTrust™ proves each individual decision. Observability answers "is the model healthy?" InferTrust™ answers "can you prove what the model did at 3:42 PM on Tuesday when it denied that patient's claim?"