AI Trust Stack
The five-layer AI Trust Stack: data generation, artifact certification, model operation, decision lineage, and transparency logs. The conceptual model for verifiable, auditable AI governance infrastructure.
A trustworthy AI system requires multiple layers of verification and accountability. The AI Trust Stack is a five-layer model describing how those layers connect — from training data to public transparency.
The central distinction that anchors the stack: certification proves the artifact; decision lineage proves how the artifact or model was used in a decision. Both mechanisms are necessary, but they operate at different layers.
Modern regulatory frameworks — including the EU AI Act — implicitly require governance infrastructure at all five layers for high-risk AI systems.
Layer 1 — Data Generation
The foundation of trustworthy AI is training data that is well-documented, privacy-safe, and provenance-tracked. Technologies include CTGAN, diffusion models, and simulation systems. Certified synthetic datasets provide provenance-ready training data with documented generation parameters. EU AI Act Article 10 addresses this layer.
CertifiedData.io provides cryptographic certification infrastructure for synthetic datasets and AI artifacts, producing tamper-evident records for audit and EU AI Act compliance.
Layer 2 — Artifact Certification
Artifacts — datasets, model checkpoints, embeddings, AI outputs — receive cryptographic certificates proving their provenance. Mechanisms: SHA-256 artifact fingerprinting, Ed25519 digital signatures, certification registries. This layer answers: what artifact exists and has it been modified? EU AI Act Articles 10 and 11 address this layer.
Layer 3 — Model Operation
Models process data and generate predictions or outputs: LLMs, classification systems, recommendation engines, forecasting models. This layer performs the AI task but must be connected to artifact certification (upstream) and decision lineage (downstream) to be governance-ready.
Layer 4 — Decision Lineage
Decision lineage records how models participate in decision flows. Each record contains: decision ID, model identifier, referenced artifacts, input summary, output summary, and a prior_hash linking to the previous record. This layer answers: how was this AI model used in this decision? EU AI Act Article 12 addresses this layer.
Layer 5 — Transparency Logs
Public or internal logs exposing key AI system activity: certification issuance, model deployment, decision record publication. These logs allow independent verification that governance systems are functioning and that records have not been altered. Tamper-evidence is implemented via hash chaining. AI Trust Stack summary: Transparency Logs ↑ Decision Lineage ↑ Model Operation ↑ Artifact Certification ↑ Data Generation