EU AI Act logging should reconstruct the consequential path

EU AI Act logging requirements for high-risk systems aim to support traceability, monitoring, risk detection, and investigation. The useful record explains what the system received, which version and identity acted, what it decided or proposed, which control responded, and what consequence occurred.

A complete prompt transcript can still miss the approval state or external side effect. It can also create a sensitive secondary data store. Design logs from the investigation question backward.

The exact legal duties depend on role and system classification. This article requires legal and privacy review.

Provider and deployer duties differ

Article 12 requires high-risk AI systems to technically allow automatic event recording across their lifetime, with events suitable for the intended purpose and relevant to risk, post-market monitoring, and deployer monitoring. Article 19 addresses provider handling of automatically generated logs under its control.

Article 26 addresses deployers of high-risk systems and requires them to keep automatically generated logs under their control for an appropriate period of at least six months, unless other applicable law provides differently. Confirm the consolidated post-Omnibus text and special rules for the exact system.

Do not apply the six-month number blindly to every AI application. Other laws and business needs may require a different period. Privacy and security owners should approve the content, access, and retention.

Record seven events around each material run

Use source identifiers where full content is unnecessary. Store sensitive fields only when the investigation or duty requires them.

Log refusals and partial failure

Most dashboards emphasize success and error. Consequential AI systems need more states.

A policy refusal is evidence that a control worked. An approval request that failed to persist is different from a rejected action. A connector success followed by a database failure creates a partial state. A repeated event blocked by an idempotency key shows duplicate protection.

Name each state and transition. Generated prose must not be the only account of what happened.

Minimize content while preserving evidence

Logs can contain personal data, confidential documents, credentials, and inferred information. Apply purpose, access, encryption, retention, deletion, and incident controls to the logging system itself.

Prefer a record such as “source document 842, version 7, permission allowed by project role” over a copied page when the identifier is enough. Hash or version material payloads where investigators need to prove which content was approved. Keep secrets and raw tokens out of logs.

Test deletion across traces, analytics, backups, and exported incident bundles. AI traceability and GDPR accountability should share facts while retaining their separate legal decisions.

Skybridge traces improved after a silent refusal

Skybridge added per-turn agent traces, component heartbeats, release revisions, and approval records as the platform matured. A specific approval failure showed what was missing: the model said a request had been submitted, while the executor had refused it because required fields were absent.

Adding a warning with workspace, source, and refusal reason made the failure observable. That one change allowed an operator to distinguish model narration, policy outcome, and storage state.

The production evidence standard connects each approval request, policy decision, execution state, and operator intervention to the released system version.

Test logs through investigation and recovery

Give an operator a run identifier and ask them to answer five questions: who requested the action, which sources shaped it, which model and policy version ran, who approved it, and whether the external consequence occurred once.

Then remove a provider, replay the event, revoke the user, and simulate a partial failure. Confirm that the trace records the safe outcome without exposing unnecessary content.

Finally, restore from backup and verify that the evidence needed for open incidents remains usable. The secure enterprise AI framework places logs inside the larger release and operation design.

Control access to the logging system through named roles. Developers may need technical errors without full business content. Security investigators may need a temporary, approved path to additional evidence. Customer administrators may need system-level activity without access to another team's confidential sources. Record every privileged investigation and close the access when the case ends.

Create an evidence dictionary so teams interpret fields consistently. “User,” “requester,” “approver,” and “executor” can refer to different identities. Define clock source, time zone, run identifier, source version, policy result, and terminal state. A technically complete log becomes weak evidence when two operators read it differently.

AI logging questions

Must high-risk AI logs contain every prompt and response?

Article 12 describes event recording suitable for traceability and specified purposes. The exact content depends on the system and law. Full transcripts may be unnecessary and risky.

How long must AI logs be kept?

Article 26 contains a high-risk deployer rule of at least six months for logs under its control, subject to applicable law. Providers, sectors, privacy rules, and incidents can create different requirements.

Are model-provider logs enough?

They cover one component. The application still needs evidence for identity, retrieval, policy, approval, execution, and outcome.