As organizations prepare for the EU Artificial Intelligence Regulation (or AI Act), the conversation typically gravitates toward high-level themes such as ethical frameworks, reducing bias, and human surveillance. However, one of the most important duties is surprisingly often overlooked. It is a requirement for systematic record keeping through logs.
While logs are typically treated as a technical by-product of software use, the AI Act elevates them to an explicit compliance mechanism for high-risk AI systems. Without robust logging, proving compliance can be impossible.
What are “logs” and why are they important for AI compliance?
In reality, logs are automatic records of what an AI system actually does. These are chronological records of events, actions, and related data inputs generated during the operation of a system. Pursuant to Rule 71 of the AI Act; High-risk AI systems must be designed to technically enable automatic recording of events over their lifetime.
Logs provide a trail of evidence of how the AI interacts with the world. Logs are important for more than just legal compliance. For AI risk mitigation purposes, logs provide traceability. You can’t fix what you can’t track.
Article 12, Paragraph 1, 2 AI Law makes clear why logging functionality is necessary. You should enable recording of events related to three main purposes:
- Identifying risks: Logging should enable the identification of situations that may pose a risk within the meaning of the AI Act. Consider an AI system used to filter job applications. If the logs reveal that the system is starting to systematically reject candidates from certain geographic areas, this may indicate a potential risk to fundamental rights. Without logs, such patterns may go unnoticed.
- Post-market monitoring: Logs support continuous evaluation of system performance after deployment. For example, if a hospital uses AI to prioritize patients in the emergency room, a review of logs may reveal that performance is degraded during high-volume night shifts. That information allows for corrective actions such as retraining and reconditioning.
- monitoring operations: Logging should support monitoring of how the system is actually used. Consider a bank that uses AI for credit scoring. Logs can show whether human reviewers are meaningfully evaluating AI recommendations or simply approving them automatically. Logging who reviewed decisions and when helps ensure that human oversight is genuine rather than a formal rubber stamp.
Essentially, logging turns an opaque “black box” into a transparent process, allowing humans to effectively monitor the system and intervene when things go off track.
Provider obligations: Design, document, and enable logging
for provider Compliance obligations for high-risk AI systems (those that develop AI or bring it to market under their own names) begin at the design stage.
- Technical integration: The system must be designed to automatically record events throughout its lifetime.
- Specific requirements for biometrics: If the system is used for “remote biometrics”, the logging standards will be significantly more stringent. Providers must ensure that they log each period of use, the relevant reference database, the specific input data that caused the match, and the identity of the natural person responsible for validating the results.
- Log Retention: Provider must retain automatically generated logs for a period appropriate to the intended purpose of the system, in each case at least six months, unless otherwise required by applicable law.
- Transparency for implementers: Recording data is only half the battle. Providers are also required to “hand over the keys”. Providers must provide adopters (entities using AI in a professional capacity) with a clear description of the mechanisms that enable the proper collection, storage, and interpretation of these logs.
- Cooperation with authorities: In the event of a regulatory investigation, Provider (or its authorized representative) has a legal obligation to provide competent authorities with access to these automatically generated logs in order to demonstrate that the system is operating in accordance with the law.
Implementer Obligations: Save and manage automatically generated logs
Providers must build high-risk AI systems that automatically record events; deployer You are actually responsible for maintaining and managing these automatically generated logs.
Once the system is up and running, log retention becomes a common compliance obligation. Providers and deployers are each required to store automatically generated logs to the extent that they have control over them.
- Operational controls: Implementers should retain logs automatically generated by high-risk AI systems to the extent that they remain under their control. This leaves a record of how the system was actually used in the field and is available for review.
- Retention Obligations: Implementers must retain automatically generated logs under their control for a period appropriate to the intended purpose of the system, in each case at least six months, unless otherwise required by applicable law.
- Sector-specific integration: For financial institutions subject to EU financial services law, logs must be maintained as part of the extensive documentation and internal governance arrangements already required by existing financial regulations.
How log storage and GDPR interact
One of the most specific requirements of the AI Act is the retention period for logs. The explicit six-month minimum requirement provides a “legal baseline” for data retention schedules.
The General Data Protection Regulation (GDPR) states that personal data must not be kept for longer than necessary, but “need” is often difficult to quantify. Many AI logs contain personal data (such as operator names, biometric match data, and certain user inputs) and are therefore subject to data protection rules.
The AI Act provides organizations with clear legitimacy for retention schedules by establishing legal requirements in Articles 19 and 26. This will allow privacy professionals to point to statutory retention floors under the AI Act and support analysis of the need to store logs for at least six months if they are under an organization’s control. However, this must always be balanced against the specificity of the data. As stated in the AI Law, this period applies unless otherwise provided for by other EU or national law, in particular the law on personal data protection.
conclusion
Whether your company is a provider or an adopter, logging should never be treated as a secondary IT concern. These are key compliance assets that bridge the gap between substantive system safety and auditable reality.
