Documentation

Detailed and organized records of processes, decisions, and configurations used to ensure accountability, traceability, and governance within an organization.

Source: AIEOG AI Lexicon (Feb 2026), adapted from NIST AI 100-1 and Model Risk Management, Comptroller’s Handbook

Documentation in the AI context is the practice of maintaining thorough, organized records of every significant aspect of an AI system throughout its lifecycle. This includes design decisions, data choices, model architecture, training procedures, validation results, deployment configurations, performance metrics, and change history.

For financial institutions, documentation is the connective tissue of AI governance. It is what transforms informal practices into defensible, auditable, and transferable processes. Without documentation, governance exists only in the minds of individual team members and disappears when they move on.

Documentation should answer the following questions at any point in time: What AI systems do we have? How were they built? What data do they use? How were they validated? How are they performing? What changes have been made? Who approved those changes?

Documentation is foundational to regulatory compliance. Examiners evaluate not just whether controls exist, but whether they are documented. An undocumented control is, from a regulatory perspective, a missing control.

The OCC’s Model Risk Management guidance explicitly requires comprehensive model documentation. The NIST AI RMF emphasizes documentation across all governance functions. For a practical look at how Treasury guidance reinforces these expectations, see our explainer. Examiners during supervisory activities routinely request model documentation, validation reports, and change logs.

Common documentation gaps in financial institutions include missing rationale for design decisions, incomplete data lineage records, insufficient validation documentation, undocumented model changes, and absent decommissioning records.

1. Define documentation standards. Establish templates and minimum requirements for each lifecycle phase: design documents, data assessments, model cards, validation reports, deployment checklists, and monitoring dashboards.
2. Document design decisions. Record why specific approaches, algorithms, data sources, and features were chosen, including alternatives considered and rationale for selection.
3. Maintain living documentation. Documentation should be updated when models change, data sources shift, or performance degrades. Static documentation quickly becomes inaccurate.
4. Make documentation accessible. Store documentation in centralized, searchable repositories accessible to compliance, risk, technology, and audit teams. Our 12 pillars of a CMS framework shows how documentation fits into a broader compliance management system.
5. Require documentation for vendor models. Third-party AI model documentation should meet the same standards as internal models. Gaps should be documented as risk factors.
6. Prepare for examination. Organize documentation so it can be produced quickly and coherently in response to examiner requests.

AI governance, AI lifecycle, Validation, Version control, AI use case inventory, AI risk assessment