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Oversight log: how to document human oversight under the EU AI Act

By Michel Venniker· · Aligned with the consolidated EU AI Act, including the 2026 Omnibus amendments.

An oversight log is the contemporaneous record that proves human oversight of a high-risk AI system under Art. 26.2 of the EU AI Act. It must capture, per oversight event, who reviewed the AI output, what they decided and why, and it must be retained for at least six months under Art. 26.6.

Updated: June 2026

Introduction: why oversight documentation matters

Art. 26.2 requires human oversighthuman oversightDesigned-in human ability to monitor, intervene in, override or shut down an AI system — meaningful only when the human has authority, information and time to act.Open full entry → of high-risk AI systems. But oversight without documentation is invisible to supervisory authorities. When the supervisory authority investigates a complaint or conducts a compliance audit, the question will not be "do you have oversight?" but "can you demonstrate oversight?" A well-maintained oversight log is your primary evidence.

This guide explains what an oversight log must contain, how to structure it, and how to maintain it efficiently in practice.

What is an oversight log?

An oversight log is a contemporaneous record of human oversight activities for high-risk AI systems. It documents that a qualified person reviewed AI outputs before or shortly after they influenced significant decisions, and records the outcome of that review.

The log serves three functions:

  1. Compliance evidence: Demonstrates to the supervisory authority and to affected individuals that meaningful oversight occurred
  2. Performance monitoring: The log data reveals patterns, rising override rates signal model degradation
  3. Learning and improvement: Override reasons documented over time build institutional knowledge about AI systemAI systemA machine-based system that, for explicit or implicit objectives, infers from input how to generate outputs — predictions, content, recommendations or decisions — that can influence physical or virtual environments. The OECD-style definition followed by the EU AI Act.Open full entry → strengths and weaknesses

What must an oversight log contain?

Minimum required elements for each oversight event:

FieldDescription
Date and timeWhen was the oversight conducted?
AI systemWhich AI system generated the output being reviewed?
Overseer identityWho conducted the oversight (name/role)?
AI output summaryWhat did the AI system recommend or decide?
Oversight decisionAccept / override / escalate
Override rationaleIf overridden: why? (required for audit trail quality)
Final decisionWhat decision was ultimately made?

Oversight frequency

Oversight frequency depends on the AI system's decision volume and risk level:

  • High-volume, high-stakes systems (credit scoring, CV screening): Oversight on every individual decision, or at minimum a structured sample of decisions with defined statistical coverage
  • Lower-volume systems (performance appraisal AI): Oversight on every decision
  • Monitoring-only systems (anomaly detection AI that generates alerts): Oversight review of all alerts before action is taken

Oversight log implementation options

  • Integrated in the AI platform: Ideal, many enterprise AI platforms have built-in human review workflows. Configure the platform to capture oversight decisions as part of the workflow.
  • Ticketing system (Jira, ServiceNow): Create oversight tickets linked to AI outputs. The ticket trail serves as the log.
  • Structured spreadsheet: Acceptable for low-volume systems. Use a shared spreadsheet with protected formatting to maintain integrity.
  • Document management system: Monthly oversight review reports filed in a versioned document system.

Retention

Oversight logs are AI system logs within the meaning of Art. 26.6. You must retain them for at least 6 months, or longer if sector-specific law requires.

Compliance checklist

  1. Is there an oversight log for every high-risk AI system?
  2. Does each log entry contain all required elements?
  3. Are override rationales documented for all overrides?
  4. Is the oversight frequency appropriate for the decision volume and risk level?
  5. Are logs retained for the required period?
  6. Is override rate data regularly analysed for performance monitoring purposes?
Legal referencesArt. 26.2Art. 26.6

More on Human oversight

Art. 14 EU AI Act: designing high-risk AI for human oversight

Reference

Art. 14 requires providers to design and build high-risk AI systems so that they can be effectively overseen by humans during use. The system must let an overseer understand its capabilities and limits, watch for anomalies, resist automation bias, correctly interpret outputs, decide not to use the system, and intervene or stop it through a kill switch (Art. 14(4)(e)). It is the design obligation that makes the deployer oversight duty of Art. 26.2 possible.

Art. 26.2 EU AI Act: human oversight of high-risk AI

Reference

Art. 26.2 requires deployers to ensure that the people assigned to oversee a high-risk AI system have the competence, training, and authority to do so effectively. Valid oversight is substantive, not formal: the overseer must understand the system, be trained on its limitations, and hold genuine authority to override its outputs.

Art. 27 EU AI Act: Fundamental Rights Impact Assessment (FRIA)

Reference

Art. 27 requires certain deployers, public bodies and private deployers in defined sectors such as credit and insurance, to conduct a Fundamental Rights Impact Assessment (FRIA) before deploying a high-risk AI system, examining the impact on fundamental rights and the mitigation measures.

Art. 4 EU AI Act: AI literacy obligations for organisations

Reference

Art. 4 has required organisations since 2 February 2025 to ensure a sufficient level of AI literacy among staff who operate or use AI systems, proportionate to the system and the role. It applies to all AI use, not only high-risk systems, and must be demonstrable.

More on Accountability

Art. 10 EU AI Act: data and data governance for high-risk AI

Reference

Art. 10 requires that the training, validation, and testing data for high-risk AI systems meets quality criteria: relevant, sufficiently representative, and as free of errors and complete as possible for the intended purpose. It also requires documented data governance practices covering collection, preparation, bias examination, and gap mitigation, and it permits the limited processing of special-category data where strictly necessary to detect and correct bias, under safeguards.

Art. 12 EU AI Act: record-keeping and logging for high-risk AI

Reference

Art. 12 requires high-risk AI systems to technically allow for the automatic recording of events (logs) over their lifetime. The logging must enable traceability of the system's functioning at a level appropriate to its intended purpose, support post-market monitoring, and help identify situations that may lead to risk or substantial modification. It is a design obligation on the provider that makes the system auditable by construction.

Art. 19 EU AI Act: keeping the automatically generated logs

Reference

Art. 19 requires providers of high-risk AI systems to keep the logs that the system automatically generates (under Art. 12) for as long as they control them, for a period appropriate to the intended purpose and at least six months unless other law requires longer. It is the retention counterpart to the Art. 12 logging capability, and it works alongside the deployer retention duty in Art. 26.6.

Art. 26.1 EU AI Act: following provider instructions as a deployer

Reference

Art. 26.1 requires deployers to use high-risk AI systems strictly in accordance with the provider's instructions for use. This means using the system only for its intended purpose, within its specified technical configuration, and by qualified users, and documenting that compliance. Deviating from the instructions can shift liability entirely to the deployer.