EU AI Act compliance needs decision evidence, not just static documentation.
The hard part of compliance is not writing that your system is governed. The hard part is showing what the system did, what evidence existed at the time, what was approved, and how reviewers can inspect the record later.
Decision Ledger is the operational layer for that problem. CertifiedData supports the artifact evidence layer underneath it, giving teams a way to connect behavior, provenance, and verification into one inspectable stack.
What regulators and auditors ultimately need
Most compliance stacks break at the evidence layer
Documentation and logs exist in most organizations, but they are rarely tied to verifiable artifacts, system state, or durable records. That creates risk during audits, investigations, and regulatory review.
Article 12 logging
See Article 12 logging in practice
Decision Ledger shows how operational records, approvals, timestamps, and evidence references can become inspectable compliance infrastructure.
System diagram
The compliance evidence stack
This architecture connects CertifiedData artifact evidence with Decision Ledger operational evidence so teams can move from claims of compliance to inspectable records.
Artifact layer
Datasets, models, prompts, outputs, and related records are fingerprinted and preserved so compliance records can reference specific system inputs and artifacts later.
Evidence layer
Hashes, signatures, certificates, timestamps, and metadata create tamper-evident proof that an artifact or record existed in a given state.
Decision layer
Decision Ledger records actions, approvals, outcomes, timestamps, and linked evidence so operational system behavior becomes inspectable.
Oversight layer
Reviewers, risk owners, and auditors inspect documentation, logs, and evidence references to assess whether controls were actually functioning.
Monitoring layer
Post-market monitoring preserves incident history, updates, interventions, and ongoing evidence continuity across the system lifecycle.
Workflow
End-to-end compliance flow
Classify the system
Determine whether the use case is likely high-risk and map the obligations that apply to the system, dataset, and deployment context.
Review risk classification →Register and certify artifacts
Fingerprint datasets and related artifacts, attach metadata, and generate machine-verifiable certification records.
Generate certified evidence →Document the system
Preserve technical documentation describing purpose, limitations, governance controls, dataset lineage, and oversight assumptions.
See documentation requirements →Capture decisions and events
Record actions, approvals, and system outcomes so Article 12-style traceability becomes operational instead of theoretical.
Start logging decisions →Verify and monitor continuously
Expose verification surfaces and maintain post-market monitoring so evidence remains inspectable over time.
See monitoring workflow →API examples
How the evidence workflow looks in practice
curl -X POST https://certifieddata.io/api/certify
curl https://certifieddata.io/api/verify/cert_12345
curl -X POST https://certifieddata.io/api/decisions/log
Artifact integrity and provenance
CertifiedData supports the evidence layer beneath the compliance stack: certification, artifact lineage, verification, and provenance.
The operational compliance layer
Decision Ledger is the stronger fit for this page because compliance systems live or die on inspectable records of actions, approvals, logs, oversight, and ongoing evidence continuity.
Related pages
Build the full compliance cluster around Decision Ledger
Make compliance inspectable
Use Decision Ledger to turn AI activity into reviewable records, and use CertifiedData where artifact certification and provenance evidence are needed underneath.