Regulators and customers are moving from AI principles to AI proof. Six artefacts that turn intent into defensible governance.
AI principles are now common. Evidence is not. As the EU AI Act, sector supervisors and enterprise procurement converge, boards are being asked to show how AI decisions are controlled — not to restate a values statement.
Six artefacts worth having
- AI system register. Every system, purpose, owner, model provider, data classification and risk tier.
- Documented risk assessments. Per system, with bias, robustness, privacy and security dimensions addressed.
- Human oversight records. Who can override, when they did, and what happened next.
- Data provenance evidence. Lawful basis, retention and consent position for training and prompt data.
- Model change control. Version history, evaluation results and approval before promotion.
- Third-party AI assurance. Vendor model documentation, sub-processor position and contractual commitments.
Structure beats enthusiasm
An AI governance forum with clear delegated authority, a defined escalation path and a standing agenda produces better outcomes than a dispersed set of well-intentioned reviewers.
Watch for shadow AI
Embedded AI features in existing SaaS tools are the most common source of ungoverned processing. Detection, then policy, then enablement — in that order.
Frameworks to lean on
ISO/IEC 42001 for the management system, the NIST AI Risk Management Framework for risk practice, and your existing ISO 27001 and ISO/IEC 27701 controls for security and privacy.
Adopt AI at pace, but adopt it with evidence.
- AI governance
- ISO 42001
- board reporting