AI decision integrity is the ability to prove, with cryptographic certainty, what an AI model decided, when it decided it, which model version ran, and what pol
AI decision integrity means that every AI inference produces a verifiable, tamper-evident record that proves exactly what the model decided, when it decided it, which model version ran, what policy was in force, how confident the model was, and what input data it saw. The record is created at the moment of inference, before any other system touches the output, and is sealed with cryptographic signatures that make post-hoc alteration detectable.
Most organizations believe they have solved AI accountability because they log decisions. But application logs, observability dashboards, and monitoring platforms share a fundamental weakness: the records they produce can be modified after the fact. There is a gap between the moment the model computes its output and the moment a separate system writes down what happened. Anyone or anything (a bug, a bad actor, a system failure) can alter what gets recorded in that gap.
Decision integrity closes that gap. The cryptographic signature is applied inside the inference event itself, before any network transmission, before any UI rendering, before any application layer processes the output. The signed record is the first thing that exists after the model computes its result.
A complete decision integrity record includes: the model version ID (a cryptographic hash of the exact model binary), the confidence score at inference time, the policy version hash, an input feature hash (proving what the model saw without storing raw data), a cryptographically bound timestamp, the decision action taken, and a sequence ID that makes deleted or reordered records visible. Together, these fields create an unforgeable receipt for every AI decision.
Any organization deploying AI to make or assist with consequential decisions in regulated industries: healthcare (clinical AI, prior authorization), financial services (credit decisions, fraud detection), automotive (autonomous vehicles), manufacturing (quality control, safety systems), architecture and construction (structural analysis), and FDA-regulated medical devices. As the EU AI Act, FDA SaMD frameworks, and sector-specific regulations mature, decision integrity is becoming a compliance requirement rather than an optional practice.
InferTrust™ (Patent Pending) is the first platform purpose-built for AI decision integrity. It signs decision records at the point of inference using device-bound cryptographic keys, stores them in append-only tamper-evident logs, and works offline on edge devices. InferTrust™ does not require changes to your AI model, training pipeline, or inference logic. It wraps existing inference calls through a lightweight SDK and produces records that satisfy regulatory, legal, and audit requirements.