AI Record Keeping Agent

The AI Record Keeping Agent

Define EU AI Act Article 12 record-keeping for a high-risk AI system — what gets logged, the event schema, retention period, access control, tamper-evidence, and review cadence — on infrastructure you control.

Explore VDF AI Agents
Art. 12Record-keeping obligation
SchemaEvent-level logging spec
Tamper-evidentTrustworthy audit trail
RetentionDefined and defensible
Specifies
Event schemaRetentionAccess controlTamper-evidenceReview cadenceAudit trail
The Traceability Problem

High-risk AI must keep records — but which, how, and for how long?

EU AI Act Article 12 requires high-risk AI systems to log automatically so their operation is traceable. The hard part isn’t enabling logs — it’s specifying what to capture, in what schema, how long to keep it, who can read it, and how to prove it wasn’t tampered with.

01

Vague logging

Most systems log for debugging, not traceability — the wrong events, no schema, no retention policy.

02

No tamper-evidence

An audit trail that can be quietly edited isn’t an audit trail. Article 12 expects records you can trust.

03

Undefined retention

Keep logs too briefly and you can’t reconstruct an incident; too long and you create privacy and cost risk.

04

Unclear access

Without access control on the logs, the record-keeping itself becomes a confidentiality problem.

The VDF AI Governance Opportunity

A record-keeping specification you can implement

Schema

What to Log, and How

Events and an explicit schema.

The agent specifies exactly which events a high-risk system should record for traceability and defines the event schema — fields, structure, and meaning — so engineering implements logging that satisfies Article 12, not just debugging logs.

  • Events to capture for traceability
  • Explicit event schema
  • Aligned to Article 12 intent
  • Implementation-ready spec
Schema
Logging Spec

Events + fields

EventsFieldsStructureMeaning

Integrity

Retention, Access & Tamper-Evidence

A trail you can stand behind.

It defines the retention period, who may access the records, and how tamper-evidence is achieved — turning "we have logs" into a defensible record-keeping regime an auditor will accept.

Tamper-evident
Trustworthy

Retention + access

RetentionAccessIntegrityAudit

Operate

Review Cadence, On-Premise

Records that stay maintained — and private.

The spec includes a review cadence so records stay meaningful over time, and runs on-premise so the logging design and the sensitive operational data it describes stay inside your perimeter.

Cadence
Maintained

On-prem · reviewed

ReviewCadenceOn-premPrivate
Where it pays back

Where record-keeping pays back

Article 12 Logging Spec

Produce the record-keeping specification a high-risk AI system needs to be traceable under Article 12.

Audit-Trail Design

Define a tamper-evident audit trail that an auditor or regulator will actually accept.

Retention Policy

Set a defensible retention period that balances traceability against privacy and cost.

Access Control Design

Specify who can read the records so the audit trail isn’t itself a confidentiality risk.

Incident Reconstruction

Ensure the right events are captured to reconstruct what a system did during an incident.

Logging Review

Assess existing logging against Article 12 and specify the gaps to close.

ROI Snapshot

What changes after rollout

Defined
What to log, and how
Tamper-evident
Trustworthy trail
Defensible
Retention policy
On-prem
Logging design stays inside
FAQ

Questions about the AI Record Keeping Agent

What is an AI record-keeping agent?

It is an AI governance agent that specifies EU AI Act Article 12 record-keeping for a high-risk AI system: what events to log, the event schema, retention period, access control, tamper-evidence, and review cadence. VDF’s agent runs on your own infrastructure so the design and the data it describes stay private.

What does EU AI Act Article 12 require?

Article 12 requires high-risk AI systems to log automatically throughout their lifecycle so their operation is traceable. The agent translates that into a concrete, implementable logging and retention specification.

Does it produce logs, or a specification?

A specification. It defines what your system should log and how — the events, schema, retention, access, and tamper-evidence — which engineering then implements. That separation keeps the design reviewable and auditable.

Why does tamper-evidence matter?

An audit trail that can be silently altered provides no assurance. The spec defines how tamper-evidence is achieved so the records are trustworthy enough to put in front of a regulator.

Is it part of the EU AI Act toolkit?

Yes — it complements VDF’s risk classification, governance policy, Annex IV documentation, transparency, training, and code-scanning agents. See the AI Governance Agents hub.

Turn "we have logs" into Article 12 record-keeping

See the AI Record Keeping Agent specify the schema, retention, and tamper-evidence your system needs.