InferTrust: Cryptographic Proof for Every AI Decision
InferTrust creates cryptographically verifiable evidence for every AI-assisted decision, helping regulated organizations prove what happened, why, and under which policy. Patent Pending.
How InferTrust Works: Four Steps to Cryptographic Proof
- AI model runs inference
- InferTrust captures decision data at the execution boundary
- Cryptographic receipt is signed with device-bound hardware key (TPM 2.0, Secure Enclave, or HSM)
- Receipt is locked into tamper-evident log with hash chain and monotonic sequence counter
Core Capabilities
Cryptographic Decision Integrity
Every inference generates a signed record using ECDSA with a device-bound key (TPM 2.0, Secure Enclave, or HSM). The record includes the model binary hash, not just a version label, so you can trace any decision back to the exact code that produced it. Signing happens at the execution boundary, before results are returned to the calling application.
Confidence-Gated Autonomous Approval
Organizations define confidence thresholds per decision type, department, or risk tier. Above the threshold, the AI acts autonomously with a signed proof of its action and confidence level. Below the threshold, the AI escalates to a human reviewer with a signed proof of the escalation. Thresholds are version-controlled and their hash is bound to every decision record.
Tamper-Evident Audit Trails
Each record chains to the previous record hash, creating a verifiable sequence. Deleting, reordering, or modifying any record breaks the chain. The monotonic Sequence ID means gaps are immediately visible. Any authorized party can verify the chain independently.
Offline-Capable Architecture
Signing uses device-bound keys that never leave the hardware. No network connectivity is required for signing or logging. Records accumulate locally and sync to a central audit store when connectivity is available. This is critical for edge devices, autonomous vehicles, factory floors, and air-gapped medical environments.
Regulatory Compliance by Design
InferTrust supports each regulation with specific architectural features:
- HIPAA: Decision records never contain PHI (only input hashes), providing a compliant audit trail
- SOX: Tamper-evident financial AI decision records with version-controlled policies
- FDA 21 CFR 820: Quality System Regulation compliance with cryptographic version tracking
- NHTSA: Real-time decision capture for autonomous vehicle incident reconstruction
- ISO 9001: Documented quality management with verifiable AI decision records
- GDPR: Data minimization (hashes, not raw data), right to explanation supported by decision records
- PCI-DSS: Cryptographic controls for AI-assisted fraud detection decisions
Why This Is Different
- Not logging. Logs record events after the fact and can be modified. InferTrust signs at the moment of inference with a hardware key.
- Not observability. Observability monitors model drift and performance trends. InferTrust proves what a specific model decided on a specific input at a specific moment.
- Not governance. Governance documents policies and processes. InferTrust proves policies were enforced at inference time.
- Not model monitoring. Model monitoring tracks accuracy over time. InferTrust creates evidence for individual decisions.
Read the full comparison: InferTrust vs. Alternatives
InferTrust by Industry
- InferTrust Clinical: Healthcare AI compliance. Reduces malpractice exposure for AI-assisted radiology reads, clinical decision support, and FDA SaMD devices (HIPAA, FDA 21 CFR Part 820, Joint Commission, FDA SaMD PCCP)
- InferTrust Payer: Health plan AI for prior authorization, claims adjudication, and utilization management (CMS Conditions of Participation, State PA Transparency Mandates, URAC)
- InferTrust Financial: Credit, fraud, and AML compliance with examiner-ready evidence (SOX, ECOA, FCRA, BSA/AML, SR 11-7)
- InferTrust Auto: Autonomous vehicle decisions sealed at the edge before any network transmission (NHTSA Safety Assessment, SAE J3016, ISO 26262, ISO/SAE 21434)
- InferTrust Industrial: Manufacturing AI for quality inspections, process control, and safety interlocks (ISO 9001, FDA 21 CFR Part 820, IEC 62443, ISO 13485)
- InferTrust AEC: Architecture, engineering, and construction AI for structural analysis and infrastructure inspections (AASHTO LRFD, IBC, ASCE 7, ACI 318)
- InferTrust FDA Enforcement: AI/ML medical device lifecycle compliance (FDA 21 CFR Part 820, FDA SaMD PCCP, GMLP, IEC 62304, ISO 13485)
Supporting Resources
Frequently Asked Questions
- How does InferTrust differ from AI observability tools like Datadog, Weights and Biases, or MLflow?
- AI observability tools monitor model performance metrics like accuracy, latency, and drift. InferTrust produces cryptographic proof that a specific AI decision happened, with a specific model version, under a specific policy, at a specific moment. Observability tells you how your models are performing. InferTrust proves what your models actually did when it matters legally.
- Does InferTrust require changes to our existing AI models?
- No. InferTrust operates at the inference boundary, wrapping your existing model inference pipeline without modifying the model itself. Integration typically takes days, not months.
- What happens if the device is offline when the AI makes a decision?
- InferTrust was designed for exactly this scenario. Cryptographic signing happens entirely on the local device using hardware-bound keys. No network connectivity is required. Records sync securely when connectivity is restored.
- How does InferTrust handle patient data and other sensitive information?
- InferTrust never stores raw sensitive data. It computes an Input Feature Hash that proves what the model evaluated without revealing the underlying information, avoiding second copies of PHI or PII.
- What regulatory frameworks does InferTrust support?
- InferTrust supports compliance with HIPAA, SOX, ECOA, FDA 21 CFR Part 820, NHTSA, SAE J3016, ISO 9001, IEC 62443, GDPR, and PCI-DSS. Each vertical edition is tailored to its industry.
- Can InferTrust records be used as evidence in litigation?
- InferTrust records are designed to meet evidentiary standards. Each record is cryptographically signed at inference time using a device-bound hardware key, creating a tamper-evident chain that traditional application logs cannot match.
- What is the performance impact of cryptographic signing on inference latency?
- The cryptographic signing adds sub-millisecond overhead using hardware-accelerated operations via TPM or Secure Enclave. For latency-critical applications, InferTrust operates within real-time constraints.
- How long does it take to integrate InferTrust?
- Most organizations complete initial integration in one to two weeks. InferTrust provides SDKs for common inference frameworks. The integration wraps your existing inference pipeline without requiring model retraining or architecture changes.