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AI Audit Trails

Instead of an AI black box:
AI with a lineage

From training data origins to every output, on:mint makes the entire lifecycle of your AI provable
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Data Governance
Input–output traceability
Without development overhead
• THE BLACK BOX PROBLEM

No one trusts an AI without proof of training data

What an AI was trained on remains invisible – to auditors, buyers, and users alike
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Auditability requires evidence

Without a documented link between input and output, AI cannot be reliably audited. on:mint establishes end-to-end governance with traceable logs.
Metallische Tulpe, Biene und Wabe

No transparency, no trus

Without provable origin and traceable use, trust in AI breaks down. on:mint makes the entire AI lifecycle transparent and auditable.
Diagramm mit Datenbanken und Pfeilen, die den Datenfluss darstellen

A transparent AI lifecycl

Misdiagnosis, bias claims, liability cases – without documented training data, you bear the risk and have nothing to refute it with.
Hochladen und Herunterladen Symbole

Auditability requires evidence

Without a documented link between input and output, AI cannot be reliably audited. on:mint establishes end-to-end governance with traceable logs.
Metallische Tulpe, Biene und Wabe

No transparency, no trus

Without provable origin and traceable use, trust in AI breaks down. on:mint makes the entire AI lifecycle transparent and auditable.
Diagramm mit Datenbanken und Pfeilen, die den Datenfluss darstellen

A transparent AI lifecycl

Misdiagnosis, bias claims, liability cases – without documented training data, you bear the risk and have nothing to refute it with.
You need end-to-end evidence – from training to output
on:mint delivers it.
• THE SOLUTION

AI Audit Trails

Case Study: From training to approval

A company trains AI systems for medical diagnostics. To obtain regulatory approval, complete and verifiable evidence is required. on:mint automatically documents every step – from training to certification.
KI-Audit-Trail-Diagramm mit Eingabedaten und Zeitstempel
01
Anchor training data in a tamper-proof way
Before training begins, the dataset is cryptographically hashed and secured on the blockchain with a timestamp. From that moment on, it is immutable.
AI-Training-Protokoll mit aktualisierten Parametern und Werten
02
Audit the training process end to end
The learning process is automatically documented – from hyperparameters to code versions. A revision-safe audit log for regulators and auditors.
Nachweis des AI-Modells NovaPharm-CertAI v1.3
03
Secure the model immutably
The AI model is cryptographically secured and blockchain-anchored. As an immutable model proof with verifiable integrity.
Zertifikat PDF-Datei, erstellt am 10. März 2026
04
Log usage continuously
Even in live operation, every model decision is logged. As an inference proof for regulators and audits.

• Use Cases

How on:mint protects your sensitive data streams

Autonomous Driving: End-to-End Documentation
AI driver-assistance systems require regulatory approval. on:mint automatically logs every training step and every model version.
Approval-ready through complete documentation
Medical AI: Transparent Documentation
Diagnostic algorithms must demonstrate which data they were trained on to gain approval. on:mint secures training data and parameters in a tamper-proof manner.
Approval-ready and audit-proof
Credit Scoring: Making Decisions Verifiable
Banks must be able to justify AI-based credit decisions to regulators. on:mint automatically logs input data, model versions, and decision logic.
Regulatorily compliant and traceable
Reproducible Experiments in AI Research
Scientific publications require traceable results. on:mint automatically documents datasets, code versions, and hyperparameters.
Scientifically sound and reproducible
Safeguarding Administrative Decisions Made by AI
Public authorities increasingly rely on AI systems. on:mint makes every automated decision transparent and documents it in an audit-proof way.
Legally secure and verifiable
Autonomous Driving: End-to-End Documentation
AI driver-assistance systems require regulatory approval. on:mint automatically logs every training step and every model version.
Approval-ready through complete documentation
Medical AI: Transparent Documentation
Diagnostic algorithms must demonstrate which data they were trained on to gain approval. on:mint secures training data and parameters in a tamper-proof manner.
Approval-ready and audit-proof
Credit Scoring: Making Decisions Verifiable
Banks must be able to justify AI-based credit decisions to regulators. on:mint automatically logs input data, model versions, and decision logic.
Regulatorily compliant and traceable
Reproducible Experiments in AI Research
Scientific publications require traceable results. on:mint automatically documents datasets, code versions, and hyperparameters.
Scientifically sound and reproducible
Safeguarding Administrative Decisions Made by AI
Public authorities increasingly rely on AI systems. on:mint makes every automated decision transparent and documents it in an audit-proof way.
Legally secure and verifiable

• Before / After

Transparency instead of a black box

Bereich
Without on:mint
With on:mint
Training data
Origin unclear; not provable
Cryptographically secured, blockchain-anchored
Development process
Retrospective documentation; incomplete
Live logging during training
Model integrity
Changes go unnoticed; no evidence
Immutable model proof, verifiable at any time
Decisions
Black box; not traceable
Every inference documented and transparently accessible
Compliance
Manual evidence; error-prone
Audit-proof and regulator-recognized

• CREATE EVIDENCE

Put an end to the AI black box

Your AI development becomes an open book. on:mint automatically logs every step and makes models eligible for regulatory certification.
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Vielfältige Gruppe von Personen und Objekten

FAQs

AI Audit Trails provide cryptographically verifiable traceability across the entire AI value chain, from training data and training runs to specific model outputs.

They make the following questions tamper-proof and auditable:

  • Which data was a model trained on?
  • Which code and model version was used?
  • Under which parameters was the model trained or used for inference?
  • Which model produced which result, and when?

All relevant steps are logged automatically, cryptographically secured, and made immutably verifiable via blockchain and gated IPFS.

This establishes transparency and accountability in AI systems.

Before training begins, the training dataset is cryptographically sealed once.

Technically, this is implemented using a Merkle tree:

  • The dataset is split into individual blocks.
  • Each block receives a hash, a cryptographic fingerprint.
  • These hashes are combined into a Merkle tree whose root uniquely represents the full dataset state.

The Merkle tree reference, together with metadata such as source, license, and timestamp, is stored in on:mint’s gated IPFS system and additionally anchored on the blockchain.

This makes it provable that a specific dataset existed in exactly this form and was referenced for training.

No claim is made that every individual data point can later be recovered from the model. With modern AI systems, this is not technically feasible today.

During training, on:mint documents the conditions and configuration of the training run, referred to as a Run Proof, including:

  • Reference to the training dataset (Merkle tree)
  • Code version, such as a Git commit or container version
  • Training parameters and configuration
  • Initial random parameters (seeds)
  • Training outcome

AI Audit Trails therefore enable verification of the training conditions, not exact reproduction of the same model. The objective is traceability and auditability, not mathematical reproducibility.

After training, each model version receives a unique cryptographic identity, known as a Model Proof:

  • The model weights are hashed.
  • This hash is stored in gated IPFS and anchored on the blockchain.
  • Any change to the model, no matter how small, results in a different hash.

As a result, it is always verifiable which exact model version was used. Undetected manipulation or silent changes are technically impossible.

During model usage, integrated AI logging automatically documents each relevant inference as an Inference Proof, including:

  • Hash of the input, such as a prompt, image, or sensor data
  • Hash of the output
  • Model ID and model version
  • Parameters, such as temperature or sampling seed
  • Timestamp and usage context

These logs are periodically bundled into audit packages, digitally signed, stored in IPFS, and anchored on the blockchain via smart contracts.

As a result, it becomes provable that a specific output was generated by a specific model, from a specific input, under specific parameters.

Got Questions? We are all ears.

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Lennart Wolf
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