Documentation of AI system design and development
Plain English Translation
Organizations must formally record the design choices and development activities for their AI systems. This documentation needs to show exactly how the system was built to meet its initial requirements and business goals, detailing things like the chosen machine learning models, system architecture, and training methods. Keeping these detailed records ensures transparency, supports future troubleshooting, and provides proof to auditors that the system was developed systematically and responsibly.
Technical Implementation
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Required Actions (startup)
- Keep basic records of the chosen machine learning models, training data sources, and system architecture.
- Ensure developers document significant design changes during the build process.
Required Actions (scaleup)
- Implement standardized templates for documenting AI system design, including hardware, software, and algorithmic choices.
- Maintain version-controlled documentation that tracks iterative changes from initial design to final architecture.
Required Actions (enterprise)
- Automate the generation of development records and training logs through CI/CD pipelines and MLOps tools.
- Enforce strict documentation requirements for threat modeling, such as mitigating data poisoning and model inversion, before deployment.
ISO/IEC 42001 Annex A.6.2.3 requires organizations to document the AI system design and development process based on organizational objectives, documented requirements, and specification criteria. This includes documenting the machine learning approach, hardware and software components, and the final system architecture.
Acceptable records include final system architecture documentation, documentation of design iterations, descriptions of machine learning methodologies, records of how the model is trained, data quality assessments, and evaluations of model refinement.
Organizations should maintain a final system architecture diagram alongside detailed design documents that justify choices regarding learning algorithms, human-AI interfaces, interoperability, and the selection of specific hardware and software components.
Organizations must document the specific machine learning approach (e.g., supervised or unsupervised), the type of learning algorithm utilized, how the model is trained, and how it is evaluated and refined throughout the iterative development lifecycle.
Traceability is maintained by explicitly linking the documented AI system design and development records back to the initial documented requirements and specification criteria, ensuring every design choice directly addresses a stated organizational objective or requirement. Tools like WatchDog Security's Compliance Center can help maintain these linkages by associating evidence artifacts to the relevant control and surfacing missing or stale documentation.
Teams should maintain version-controlled records of model training workflows, detailing the data used, data quality measures, training parameters, and iterative refinements, to demonstrate that the final model was developed deliberately, reproducibly, and in alignment with specifications. Tools like WatchDog Security's Policy Management can support controlled document updates and approvals while preserving a clear version history for auditors.
Design/development documentation (A.6.2.3) captures the historical process, architecture choices, and iterative building of the system. Technical documentation (A.6.2.7) is the resulting manual or package created for interested parties, users, or authorities to understand how to operate and monitor the deployed system.
Documentation should be updated continuously throughout the development lifecycle to capture multiple iterations. Once finalized, it should be formally reviewed and updated whenever material enhancements or significant changes are made to the system's architecture or algorithms. Tools like WatchDog Security's Policy Management can help schedule reviews, track attestations, and ensure teams are working from the latest approved version.
Evidence should include version histories of design documents, sign-offs at various development stages, records matching final architectural capabilities to initial specification criteria, and documentation showing how specific security threats (like data poisoning or model inversion) were considered during design.
Design documentation should explicitly detail how identified risks and security threats are mitigated architecturally, and provide the foundational design parameters that verification and validation testing will later evaluate the system against to ensure safety and effectiveness.
Audit readiness depends on consistent structure, controlled updates, and easy retrieval of the latest approved design artifacts. Tools like WatchDog Security's Policy Management can help standardize documentation templates, enforce version control, and track reviews/approvals so design and development records stay current and traceable.
Traceability is strongest when each major design choice is tied to a specific requirement, risk, and treatment decision in a single system of record. Tools like WatchDog Security's Risk Register can document risks and treatments and reference the corresponding design artifacts, making it easier to show auditors why particular architectural or model choices were made.
| Version | Date | Author | Description |
|---|---|---|---|
| 1.0.0 | 2026-02-23 | WatchDog Security GRC Team | Initial publication |
