Record-Keeping Obligations Under Article 12 AI Act
Article 12 of the EU AI Act requires providers and deployers of high-risk AI systems to implement automatic logging of all system-relevant events throughout the lifecycle. We support you in building compliant logging systems, audit trail structures and retention policies.
- ✓Implement automatic logging per Article 12 AI Act requirements
- ✓Meet retention periods for providers (10 years) and deployers (6 months)
- ✓Build audit trail structures for traceability and post-market monitoring
- ✓GDPR-compliant logging strategy with data minimisation and purpose limitation
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What record-keeping obligations apply under Article 12 AI Act for high-risk AI systems?
Our Expertise
- Specialised knowledge of EU AI Act record-keeping requirements
- Experience in the technical implementation of compliance systems
- Comprehensive approach covering legal through to technical aspects
- Proven implementation strategies and best practices
Compliance Notice
Incomplete or inadequate records can lead to significant fines under the EU AI Act. A proactive record-keeping strategy is essential for compliance.
ADVISORI in Numbers
11+
Years of Experience
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Employees
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Projects
We develop tailored record-keeping solutions that meet regulatory requirements and can be integrated into your existing systems.
Our Approach:
Assessment of your AI systems and record-keeping requirements
Design of a comprehensive documentation strategy
Implementation of automated record-keeping systems
Integration into existing IT infrastructures
Testing, validation, and continuous monitoring
Asan Stefanski
Head of Digital Transformation
Expertise & Experience:
11+ years of experience, Applied Computer Science degree, Strategic planning and management of AI projects, Cyber Security, Secure Software Development, AI
Our Services
We offer you tailored solutions for your digital transformation
Record-Keeping System Design
Development of a comprehensive documentation strategy and technical architecture for EU AI Act-compliant records.
- Analysis of specific documentation requirements
- Design of automated logging architectures
- Data modelling for structured records
- Integration into AI development workflows
Technical Implementation
Implementation of solid and flexible record-keeping infrastructures with automated processes.
- Automated data capture and storage
- Secure and immutable audit trails
- Real-time monitoring and alerting
- Compliance dashboard and reporting
Our Competencies
Choose the area that fits your requirements
Article 10 of the EU AI Act imposes strict requirements on training, validation and test data for high-risk AI systems. We support you in building data governance that ensures data quality, detects bias and meets the documentation obligations under the AI Regulation.
Article 14 of the EU AI Act requires providers and deployers of high-risk AI systems to implement effective human oversight. We help you establish human-in-the-loop processes, stop mechanisms, and monitoring frameworks — compliant by the 2 December 2027 deadline.
The EU AI Act requires solid risk management systems for high-risk AI systems. We support you in developing and implementing comprehensive, compliance-conformant risk control processes.
The EU AI Act places high demands on the technical documentation of high-risk AI systems. We support you in creating comprehensive, compliance-conformant documentation that meets all regulatory standards.
Frequently Asked Questions about EU AI Act Record Keeping
What record-keeping obligations does the EU AI Act impose?
Article
12 requires high-risk AI systems to have automatic logging capabilities that capture events throughout the entire lifecycle. Records serve traceability, risk detection, and post-market surveillance. They must be implemented by default and function without external modification.
What must an AI system automatically log?
Automatic logging must capture at minimum: usage periods (start and end timestamps), input data leading to matches, reference databases used, and identity of verifying persons. Biometric identification systems under Annex III point 1(a) face enhanced logging requirements.
How long must AI records be retained?
Retention periods differ by role: providers must retain technical documentation and quality management records for at least
10 years. Deployers must store automatically generated logs for at least
6 months under Article 26(6). Sector-specific regulations may require longer periods for certain high-risk systems.
What are AI records used for?
Records serve three core functions: risk detection (identifying events indicating emerging risks), post-market monitoring under Article
72 (assessing system behaviour after deployment), and operational oversight under Article 26(5) (facilitating monitoring by deployers and authorities).
What standards exist for AI system logging?
The final draft ISO/IEC FDIS
24970 (as of May 2026) standardises AI system logging and helps providers integrate Article
12 into development and design processes. Additionally, EN 18229–1 defines requirements for internal logging mechanisms. The European Commission is developing harmonised standards that will serve as presumption of conformity with logging requirements.
How do AI record-keeping duties relate to GDPR?
AI logs may contain personal data subject to GDPR. Storing records must be compatible with data protection principles, particularly data minimisation and purpose limitation. An integrated logging strategy addressing both the AI Act and GDPR frameworks is essential for compliance.
When do EU AI Act record-keeping obligations take effect?
The Digital Omnibus on AI, adopted in June 2026, moved the record-keeping obligations for standalone high-risk AI systems under Annex III from
2 August
2026 to
2 December 2027. For high-risk AI embedded in products covered by Annex I product-safety law, the date is
2 August 2028. The Article
50 transparency obligations were not deferred and have applied since
2 August 2026. Providers should still build logging into development now, because retrofitting existing systems is significantly more complex and costly.
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