Jamie
Regulators, enterprise buyers, and internal audit teams are asking the same question: can you prove your AI systems are controlled? The organizations answering yes built governance into daily operations—not slide decks.
Here is a practical framework for audit-ready AI: inventory what you have, document decisions, monitor in production, and respond to incidents with a defined playbook.
1) Create an AI Inventory
Track for every production AI feature: model/provider and version, use case and owner, data sources, intended users, and risk classification (low/medium/high). Update the inventory when anything changes—treat it like your software bill of materials.
2) Maintain Decision and Evaluation Records
Keep evaluation datasets and results, documented known limitations, a log of prompt and data changes, and human review policies. When auditors ask “what did you know at launch?”, you want a timestamped answer.
AI audit frequency by industry
Finance and healthcare lead—expect scrutiny to spread across sectors
Deloitte regulated AI compliance survey, 2025
Compliance framework adoption trend
NIST RMF and ISO 42001 adoption accelerating year over year
ISO 42001 + NIST RMF adoption index
AI incident report categories
Bias and privacy lead—build monitoring for both from day one
Aggregated AI audit findings, 2024–2025
3) Monitor Performance and Incidents
Governance in production means monitoring dashboards, feedback triage, incident response workflows, and retraining triggers when metrics drift beyond thresholds.